Momentum for Buy-and-Hold Investors

March 25, 2016, 10:54 am

≫ Next: What You Should Remember About the Markets

≪ Previous: Dual Momentum and Dollar Cost Averaging

There are many investors who prefer to remain invested in stocks at all times. Perhaps they think tactical allocation is some kind of voodoo. Maybe they have a strong psychological bias against occasional whipsaw losses and do not mind bear market drawdowns. Maybe they have institutional constraints requiring them to always be in stocks. Whatever the reason for their buy-and-hold orientation, let us see how they or anyone can use relative momentum (half of dual momentum) to get improved investment results.

Our core holding will be the S&P 500. To use relative momentum, we need at least two assets. We will use the MSCI All Country World Index ex-US (ACWI ex-US) as our second one. Each month we will invest in whichever of these two has performed better over the preceding 12 months.

Here are the results from January 1971 through February 2016 for this simple momentum approach rebalanced monthly. Relative momentum allocates to the S&P 500 55% of the time and to the ACWI ex-US 45% of the time. Transaction costs are negligible, since there is on average less than one trade per year.

	Momentum	S&P 500	ACWI ex-US	EqualWeight
Annual Return	14.5	11.5	11.5	11.5
Standard Deviation	16.1	15.2	17.3	14.8
Sharpe Ratio	0.51	0.36	0.32	0.37
Worst Drawdown	-54.6	-51.0	-57.4	- 54.2

Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Please see our Disclaimer page for more information.

Using relative momentum, there is almost a 300 annual basis point increase in return compared to holding each asset or a blend of both assets! Please look closely at the following performance chart to see how these increased profits come about.

How We Earn Momentum Profits

From 1975 through 1990, ACWI ex-US outperformed the S&P 500. Relative momentum invested in the ACWI ex-US then similarly outperformed. The S&P 500 did better than ACWI ex-US from 1990 through 2000. Relative momentum switched over to the S&P then and so also outperformed the ACWI ex-US. Both indices moved together until 2003 when ACWI ex-US outperformed the S&P 500. Momentum switched back to the ACWI ex-US and also beat the S&P 500. In 2009, the S&P 500 took the lead again ,and momentum once again moved higher with the S&P 500. It is as if each time a faster train comes along, relative momentum hops on board to win the investment race.

Who would not do something like this to earn an extra 300 basis points per year? You could easily do this strategy using just broad-based U.S. and non-U.S. stock funds in 401K retirement accounts and variable annuity contracts. If you do not want to bother checking SharpCharts monthly to determine the funds’ annual returns, you can set up a free account with Morningstar. They will regularly email you the annual return information.

Why Global Diversification Works

The only concern I have heard about this strategy is that the world is now more globalized. There may no longer be as much to gain from geographic diversification. It is true that many large corporations derive a significant amount of their revenue from international operations. But corporate profits have little to do with the difference in return between U.S. and non-U.S. stocks. As the following chart shows, the relative performance of these markets depends largely on the strength or weakness of the U.S. dollar.

Chart courtesy of SharpeReturns.ca

When the U.S. dollar is strong, U.S. stocks tend to outperform non-U.S. stocks. Non-U.S. stocks outperform when the U.S. dollar is weak. Our simple relative momentum strategy takes advantage of global macro-economic trends. Just as it does not make sense to be simultaneously long and short the U.S. dollar, so it not the best idea to be long U.S and non-U.S. stocks at the same time. It would be better to own U.S. stocks when the U.S. dollar is strong, and to own non-U.S. stocks when the U.S. dollar is weak. Relative momentum automatically puts us on the right side of this macro-economic trend. There is no need to pay for global macro management.

Two Types of Diversification

There are two types of diversification in the world of investing. The usual method of vertical diversification stacks one asset up on top of others. It owns them all at the same time, which means some will underperform and create a drag on performance.

Momentum uses horizontal (or temporal) diversification that invests only in the strongest asset(s). As we saw in the chart above, this translates into higher returns as we move forward in time and rotate our exposure to the strongest asset. Momentum thus depends on persistence in performance. Momentum improves the performance of nearly every asset class from the year 1800 to the present [1].

The question then is should we use momentum in a macro manner as shown above, or apply it to individual stocks as done by most momentum and multi-factor funds? Multi-factor approaches are now becoming especially popular. (See our blog post, “Multi-Factor Investing.”)

Macro Momentum versus Stock Momentum + Value

Let us compare our macro momentum strategy to a multi-factor approach with individual stocks. We will use the two most popular factors: value and momentum. Value and momentum represent the two strongest anomalies. Others have also argued that they complement each other and perform well together.

For value, we use the MSCI USA Value Index that selects the top half of large and mid-cap stocks on the basis of price-to-book, price-to-forward earnings, and dividend yield. This index currently holds 319 stocks and has a 17% annual turnover.

For momentum, we use the MSCI USA Momentum Index that selects the top 30% of large and mid-cap stocks based on a combination of 6 and 12-month momentum adjusted for volatility. This index currently holds 122 stocks and has an annual turnover of 137%.

Both MSCI indices rebalance semi-annually and began in January 1975. None of the indices accounts for transaction costs. Here is our macro momentum strategy compared to the 50/50 split between these two MSCI indices from January 1975 through February 2016.

	Momentum	MSCI 50/50	S&P 500
Annual Return	15.7	14.2	13.0
Standard Deviation	15.6	15.0	15.1
Sharpe Ratio	0.39	0.32	0.25
Worst Drawdown	-54.6	-52.5	-51.0

Our macro momentum approach had the highest Sharpe ratio. It returned an average annual 150 basis points above the combined stock momentum plus value portfolio. It also had a strong 270 basis point annual return advantage over the S&P 500.

Scalability and Trading Costs

But that is not the whole story. We should also consider scalability issues and transaction costs. Holding fewer stocks and rebalancing more frequently leads to higher returns. The following table shows compound annual growth rates (CAGRs) of value-weighted portfolios using a universe of the largest 500 U.S. stocks.

Table courtesy of AlphaArchitect

The ideal stock momentum portfolio is highly concentrated and rebalances monthly. Momentum fund managers know this, and nearly all of them limit the size of their momentum portfolios to 150 or fewer stocks. Ten of the twelve momentum funds also rebalance their portfolios at least quarterly.

The MSCI momentum index that rebalances semi-annually rather than quarterly has an annual turnover of 137%. Passive indices like the S&P 500 or the ACWI ex-U.S. have an annual turnover of only around 3%.

In 2006, there were no publicly available momentum funds. Today there are a dozen funds dedicated to U.S. stock momentum. There are also more than a dozen multi-factor funds using momentum (see “Multi-Factor Investing”). Every month our friends at AlphaArchitect post the top 100 momentum stocks. So momentum investing with individual stocks is no longer a neglected strategy.

In their paper, “Are Momentum Profits Robust to Trading Costs?” Korajczyk and Sadka (2004) show that momentum profits drop to zero once the amount of momentum assets reaches $2 to $5 billion. We are already well past that level. Imagine what will happen when hundreds of billions of dollars tries to trade the same small number of momentum stocks each quarter.

Related to scalability is the issue of transaction costs. In “The Illusory Nature of Momentum Profits,” Lesmond, Schill, and Zhou (2002), use a conservative procedure to estimate annual stock momentum trading costs at nearly 7%. This reduces stock momentum profits down to near zero. Momentum is not only a high turnover strategy, but momentum stocks are often more volatile and have higher bid ask spreads.

Frazzini, Israel, and Moskowitz (2012) of AQR show that momentum trading costs are manageable based on AQR’s own 12 years of proprietary transaction data. But in the latest published research, Fisher, Shah, and Titman (2015) use observed bid-ask spreads and say, “Our estimates of trading costs are generally much larger than those reported in Frazzini, Israel and Moskowitz (2012), and somewhat smaller than those described in Lesmond, Schill and Zhou (2004) and Korajczyk and Sadka (2004).” Also, Jason Hsu PhD, co-founder of Research Affiliates, has a forthcoming report showing large-cap stock momentum returns with quarterly rebalancing being less than the large-cap market return due to trading costs.

Since our relative momentum strategy uses stock indices, scalability is not an issue. We can trade almost unlimited amounts of capital in broad-based U.S. and non-U.S. stock index funds with hardly any impact on trade executions. Transaction costs are also a moot point. There is less than one trade per year with this approach.

The simple momentum strategy presented above can help buy-and-hold investors meet their investment goals without the uncertainties associated with high transaction costs, scalability, or other similar factors. Even without adjustments for those factors, our simple momentum strategy showed an annual 150 basis point advantage over stock momentum combined with value, and a 270 basis point advantage over the S&P 500 index.

[1] See Geczy and Samonov (2015),“215 Years of Global Multi-Asset Momentum: 1800-2014 (Equities, Sectors, Currencies, Bonds, Commodities and Stocks)”. They show that momentum is consistent and robust. It works with and across different asset classes. It works best, however, with stock indices.

↧

What You Should Remember About the Markets

April 28, 2016, 7:01 pm

≫ Next: Smart Beta Is Still Just Beta

≪ Previous: Momentum for Buy-and-Hold Investors

Because I have been an investment professional for more than 40 years, I sometimes get asked my opinion about the markets. These questions usually come from those without a systematic approach toward investing. Here are some typical questions and answers:

Question: How much do you think the stock market can drop?
Response: 89%
Question: What?!!
Response: Well, that is the most it has dropped in the past. But past performance is no assurance of future success, so I guess it could go down more than that.

Question: I just looked at my account, and it is down. What should I do?
Response: Stop looking at your account.

Question: What are you doing now?
Response: What I always do … following my models.

After these responses, I am usually not asked any more questions.

Simple But Not Easy

Some say investing is simple, but not easy. This is due to myopic loss aversion, which combines loss aversion, where we regret losses almost twice as much as we appreciate gains, with the tendency to look at our investments too frequently.

We should remember that we cannot control the returns that the markets give us, but we can control what risks we are willing to accept. If we do not have systematic investment rules, it is easy to succumb to our emotions that cause us to buy and sell at inappropriate times. The annual Dalbar studies show that investors generally make terrible timing decisions.

But investing does not have to be difficult if we have firm rules in place to keep us in tune with market forces. A sailor cannot control the wind, but she can determine how to take advantage of it to get her where she wants to go.

I have found the most important principle to keep in mind is the old adage “the trend is your friend.” As some say, "the easiest way to ride a horse is in the direction it is headed." To remind me of how important it is to stay in tune with the long-term trend of the markets, I have this on my office wall:

Source: Quotatium.com

Trend Following

Many are familiar with this saying, but few have the ability to always adhere to it. Much of Warren Buffett’s success is because he had the vision to stick with his approach over the long run no matter how the markets treated him. Buffett once said, “You don’t have to be smarter than the rest. You have to be more disciplined than the rest.” This discipline applies not only to staying with existing positions. It also means re-entering the market when your approach calls for it, even though uncertainties may still exist.

What gives me the ability to stay with the long-term trend of the market? First is knowing how well trend following has performed in the past. Let us now look at that, as well as how I determine the trend of the market.

Absolute Momentum

There are different approaches to trend following, such as moving averages, charting patterns, or various technical indicators. The trend following method I prefer is absolute (time-series) momentum. It has some advantages over other forms of trend following. First, it is easy to understand and to back test. It looks at whether or not the market has gone up or down over your look back period.

In my research going back to 1927, absolute momentum had 30% fewer trades than comparable moving average signals. From 1971 through 2015, our Global Equities Momentum (GEM) dual momentum model had 10 absolute momentum trades that exited the stock market and had to reenter within a 3 month period. A 10-month moving average had 20 such exits and reentries. The popular 200-day moving average had even more signals. Fewer trades mean lower frictional costs and fewer whipsaw losses.

You do not need to enter and exit right at market tops and bottoms to do well. In fact, if your investment approach is overly sensitive to price change and tries to enter and exit too close to tops and bottoms, you will often get whipsawed.

Because of whipsaw losses and lagging entry signals, trend following often underperforms buy-and-hold during bull markets. This is the price you pay for protection from severe bear market risk exposure.

But since absolute momentum has a low number of whipsaw losses, the relative momentum part of dual momentum can put us ahead in bull markets over the long run. Absolute momentum can then do its job by keeping us largely out of harm’s way during bear markets. The tables below show how absolute momentum, relative momentum, and dual momentum (GEM) have performed during bull and bear markets since 1971. Dual momentum has outperformed in bull markets while converting bear market losses into crises alpha profits.

Bull and Bear Market Performance January 1971 - December 2015

Bull Markets

	S&P 500	Abs Mom	GEM
Jan 71-Dec 72	36.0	32.6	65.6
Oct 74-Nov 80	198.3	91.6	103.3
Aug 82-Aug 87	279.7	246.3	569.2
Dec 87-Aug 00	816.6	728.4	730.5
Oct 02-Oct 07	108.3	72.4	181.6
Mar 09-Jul15	227.7	136.8	106.4
Average	277.7	218.1	292.7

Bear Markets	S&P 500	Rel Mom	GEM
Jan 73-Sep 74	-42.6	-35.6	15.1
Dec 80-Jul 82	-16.5	-16.9	16.0
Sep 87-Nov 87	-29.6	-15.1	-15.1
Sep 00-Sep 02	-44.7	-43.4	14.9
Nov 07-Feb 09	-50.9	-54.6	-13.1
Average	-36.9	-33.1	3.6

Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Please see our Disclaimer page for more information.

Robustness

My research paper, “Absolute Momentum: A Simple Rule-Based Strategy and Trend Following Overlay” showed the effectiveness of absolute momentum across eight different markets from 1974 through 2012. Moskowitz et al (2011) demonstrated the efficacy of absolute momentum from 1965 through 2011 when applied to equity index, currency, commodity, and bond futures. In “215 Years of Global Asset Momentum: 1800-2014,” Geczy & Samonov (2015) showed that both relative and absolute momentum outperformed buy-and-hold from 1801 up to the present time when applied to stocks, stock indices, sectors, bonds, currencies, and commodities.

Greyserman & Kaminski (2014) performed the longest ever study of trend-following. Using trend following momentum from 1695 through 2013, they found that stock indices had higher returns and higher Sharpe ratios than a buy-and-hold approach. The chance of large drawdowns was also small compared to buy-and-hold. The authors found similar results in 84 bond, currency, and commodity markets all the way back to the year 1223! Talk about confidence building. These kinds of results are what give me the ability to stay with absolute momentum under all market conditions.

Market Overreaction

I have some clients though who are less familiar with and sanguine about trend following. They still get nervous during times of market stress, such as August of last year. They need to also understand that stocks do not trend all the time. The stock market can overextend itself and mean revert over the short run. During such times it is important for investors to stay the course and not overreact to short term volatility.

To remind me to remind others about short-term mean reversion, I have this coffee mug in my office:

Source: Quotatium.com

This tells me to ignore market noise and calmly accept occasional market overreactions that are usually followed by mean reversion. There is no way to get rid of short-term volatility and still earn high returns from our investments. We should, in fact, embrace short-term volatility since it is what leads to superior returns over the long run.

What to Remember

Rigorous academic research confirms the existence of trend persistence and short term mean reversion. Whatever your investment approach, if you respect these two forces you should be able to invest with comfort and conviction. Being aware of these principles gives us the two qualities required for long run investment success. First is the discipline we need to follow your proven methods unwaveringly.

The second is patience.

Warren Buffett said the stock market is a mechanism for transferring wealth from the impatient to the patient. Like Buffett, we also need to patiently accept inevitable periods of short-term volatility and underperformance with respect to our benchmarks.

If you have trouble always remembering the concepts of trend persistence and mean reversion, then do what I do. Get yourself a poster and coffee mug.

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Smart Beta Is Still Just Beta

June 13, 2016, 6:15 am

≫ Next: Most Useful Investment Blogs

≪ Previous: What You Should Remember About the Markets

Some say that bull markets climb a wall of worry. This is good news for those already in the market. Worriers will help the market go higher later when they finally decide to jump on the bandwagon. Herding and regret aversion (fear of losing out on future profits) can eventually overcome loss aversion.

Investors Skeptical

The iconic investor and money manager, Sir John Templeton, said, “Bull markets are born on pessimism, grow on skepticism, mature on optimism, and die on euphoria.” The current bull market in U.S. stocks, though longer in duration than many previous bull markets, has not yet garnered a lot of investor confidence. It is still in the skepticism stage. Perhaps investors have remained fearful due to the two bear markets of the past 20 years when stocks lost half their value each time.

Even though the U.S. stock market is around new highs, investors are still skeptical about further gains lying ahead. According to the AAII Sentiment Survey, at the end of May the percentage of individual investors optimistic about stock market gains was at its lowest level in 11 years.

Investment flows have also reflected lackluster investor interest. Only 52% of U.S. adults are invested in the stock market. This is tied with 2013 as the lowest level in 16 years. The cumulative flow into mutual funds and ETFs is 25% lower than it was 18 months ago. Among professional money managers, allocations to equities are near an 8-year low, and cash levels are near an all-time high.

Overvalued Stocks

With the U. S stock market at new highs, sentiment has shifted from the market being in a “distributional top” pattern to it being “overpriced.” High valuations may mean lower expected returns over the next 10 years, but it does not mean valuations cannot get even higher. In April 1996, the Shiller CAPE ratio was at 25, near where it is today. But the CAPE ratio continued to rise over the next 3 years until it reached a high of 43 in November 1999. The S&P 500 gained another 138% during that time.

From that level, the S&P 500 lost 9% over the next 10 years. But look at what happened with our Global Equities Momentum (GEM) model that took advantage of shorter-term fluctuations in stocks and bonds to earn extraordinary returns during that time.

Source: Sharpereturns.ca Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Please see our Performance and Disclaimer pages for more information.

Smart Beta and Low Volatility

Since most investors are not familiar with the benefits of dual momentum, they have gravitated toward factor-based “smart beta” funds. According to Morningstar, the amount of assets in smart beta funds grew from $103 billion in 2008 to $616 billion at the end of 2015. As of October 1 of last year, $110 billion of that was in “low-volatility” funds, which investors may think lessen the risks of investing.

BlackRock projects that smart beta ETF assets will reach $1 trillion globally by 2020 and $2.4 trillion by 2025 [1]. This is an annual growth rate of 19%, double that of the overall ETF market. Low volatility and factor (multi and single) funds are expected to be key drivers of this growth. They represent more than 60% of new smart beta inflows through 2025.

Smart beta ETFs saw $31 billion in new fund flows globally in 2015 with minimum low volatility ETFs accounting for $11 billion of it. The largest of these low volatility ETFs, the iShares Edge MSCI Min Vol USA ETF (USMV) has $13 billion, and 40% of those assets were contributed this year.

The large inflow of capital into low volatility stocks has bid up the return of USMV for 2016 to 8.6% versus 3.5% for the iShares S&P 500 ETF (IVV). The P/E ratio of USMV on a trailing 12-month basis is now 24.8 versus 18.8 for the S&P 500. Arnott et al. (2016) show that high valuations of factor and smart beta strategies are negatively correlated with future returns. Investors jumping on the low volatility bandwagon now may be disappointed when prices return to more normal levels.

Smart Beta Issues

What about the advantages that smart beta in general are supposed to give investors? A Vanguard study showed that smart beta excess returns are explained by time varying exposure to risk factors such as style and size. Glushkov (2015) explored this further. He looked at the performance of 164 smart beta ETFs from 2003 through 2014 with respect to benchmarks based on size, value, momentum, quality, beta, volatility and other factors. He found there is no conclusive evidence that smart beta ETFs outperformed their risk-adjusted benchmarks over this period.

Data Overfitting

Backtest overfitting is also a serious problem for smart beta strategies. Suhonen et al. (2016) examined 215 smart beta strategies across five asset classes. They found a median 73% deterioration in the Sharpe ratio between backtest and live performance periods.

Source: Suhonen et al. (2016)

The deterioration of Sharpe ratios was most pronounced among the most complex strategies. Their reduction in Sharpe ratios was 30% higher than those of the simplest strategies. As other research has shown, intensive back testing and complex modeling often pick up more on noise patterns in the data than on the underlying signal processes.

Unrecognized Risks

Very few still believe that the markets are perfectly efficient. Since there is plenty of contrary evidence now, many think it is not difficult to do better than the market. This can be a costly mistake. Most investors would be better off holding low-cost passive index funds than what they are doing. We should remember that smart beta is still just beta. It does not give higher risk-adjusted returns.
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Furthermore, we can define risk in different ways. Academics equate risk with volatility, but that is too limiting. Long Term Capital Management, founded by academics, did well by exploiting derivative mispricing. But unforeseen liquidity risk wiped out all their gains and most of their capital. It also nearly led to the collapse of the world’s financial system.[2]

There are also unrecognized risks among the more popular investment factors. Some people were surprised that value and momentum were left out of Fama and French’s latest factor pricing model. But value investing has had eight steady years of severe benchmark underperformance. I call this kind of tracking error “relative performance risk.” It may explain why investors need higher returns from value investing.

There are unrecognized risks with stock momentum investing as well. Momentum works best with focused portfolios of 100 or fewer stocks and when portfolios are rebalanced frequently. There is now substantial capital invested in single and multi-factor funds that use stock momentum. More capital is coming into momentum at an increasing rate. Every month AlphaArchitect freely discloses (to self-described investment professionals) on their website the top 100 momentum stocks. But stock momentum is a high turnover strategy with 25-30% of the portfolio typically replaced with every rebalance. There is bound to be a significant scalability problem when hundreds of billions of dollars tries to enter and exit the same 25 or 30 stocks each quarter.

Momentum also favors volatile stocks with wide bid/ask spreads. Wide spreads combined with high portfolio turnover lead to high transaction costs that can eliminate much of the excess return we see when we backtest momentum strategies.

Sensible Alternatives

Markets are not easy to beat when you consider all the risks. Ironically, many investors in smart beta or other actively managed funds pay higher expense ratios in order to underperform. Compare that to the cap-weighted Schwab U.S. Large-Cap ETF (SCHX) that holds 750 stocks. Its annual portfolio turnover is just 4%, and its expense ratio is only .03%. That makes SCHX hard to beat as a buy-and-hold investment.

For those interested in a momentum slant, keep in mind that cap-weighted indexes have a built in momentum bias without scalability or transaction cost issues. As small companies grow and prosper, they naturally become an increasing part of a cap-weighted portfolio, while poor performers receive less weighting over time.Cap-weighting lets your profits run on and cuts your losses short.

For example, the largest stock holding in the S&P 500 index is Apple. It is worth more than General Electric, General Motors, and McDonalds combined and more than the 100 smallest holdings combined. How many bought Apple in December 1982 when it became part of the S&P 500 index at a price of 48 cents a share (adjusted for dividends) and have held it continuously since then? Investors often prefer more complicated approaches that sound good, like smart beta or multi-factor funds, but simpler usually is better.

[1] BlackRock Global Business Intelligence, May 10, 2016
[2] See When Genius Failed: The Rise and Fall of Long-Term Capital Management by Roger Lowenstein (2000).

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Most Useful Investment Blogs

August 4, 2016, 5:50 am

≫ Next: Risk Tolerance Assessment

≪ Previous: Smart Beta Is Still Just Beta

As with many people these days, most of my investment information comes from the internet. It has taken me years to compile a group of research-oriented blogs and websites that I have found most useful. Here is my annotated list:

Investment Blogs

Quantocracy: This is an aggregator of quantitative trading links to blog posts and research articles. It covers a broad range of ideas from coding to theory. So not everything will be of interest to everyone. But you can evaluate links quickly, since Quantocracy displays the title and first sentence of each article. Those with any interest in quantitative finance should check out this blog.

Abnormal Returns: This is another aggregator with short content summaries. It is broader in scope than Quantocracy. In fact, about one-quarter of the links have nothing to do with investing. But many are interesting anyway. The blog's daily emails make it easy to find articles of interest.

CXO Advisory: This website is a good way to learn about new investment research posted on the Social Science Research Network (SSRN). If you pay a modest subscription fee, you can read CXO’s analysis of these research papers, which is a big time saver. CXO sometimes does book reviews andr researches other ideas, including momentum.

Quantpedia: Useful for summaries and excerpts of investment research that may not show up on CXO. They use other sources besides SSRN, such as the Cornell University Library.

Alpha Architect: This blog is like an aggregator in that they put out posts almost every day, and many of these cover other people’s research without critical analysis. Other posts contain Alpha’s own research and insights. Wes and his crew try to democratize investing and make academic concepts understandable to the public.

EconomPic Data: This was once one of the most popular finance blogs. It became dormant for a time due to work constraints on its author, Jake. Now it is back stronger than ever. Jake usually has thought provoking things to say, and he does some good research. His site is momentum friendly. Jake is also quite active on Twitter.

A Wealth of Common Sense: The author, Ben Carlson, is a member of the Ritholtz posse that includes The Big Picture and The Reformed Broker. All these are interesting , but Ben’s site is my favorite. It does indeed offer a wealth of common sense.

Sharpe Returns: This is the only blog besides my own that focuses on dual momentum. It’s author, Gogi, comes up with original ideas of his own, such as:
1) performance difference between U.S. and non-U.S. stocks depends on the strength of the U.S. dollar
2) dual momentum can do well even during those decades when stocks are overvalued

3) long term performance can be seriously distorted by short term performance, as in the case of gold.

Twitter

Twitter is also an excellent source of investment information. Not only do those followed on Twitter offer their own insights, but they retweet and comment on worthwhile information from others. Here are the Twitter handles of the above bloggers plus my own:

Quantocracy @quantocracy
Tadas Viskanta @abnormalreturns
CXO Advisory @CXOAdvisory
Wesley R Gray @alphaarchitect
Jake @EconomPic
Ben Carlson @awealthofcs
Gogi Gerwal @sharpeReturns
Gary Antonacci @Gary Antonacci

Here are others I like who have many followers:

Meb Faber @MebFaber
Vanguard Advisors @Vanguard_FA
Cullen Roche @cullenroche

These have fewer followers but deserve more:

Samuel Lee @etfsamlee
The Leuthold Group @LeutholdGroup
Ned Davis Research @NDR_Research

There are many other excellent investment bloggers and Twitter peeps. I follow around 90. Any more and I would not have time to read them all. As with other things in life, you need to find the right balance.

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Risk Tolerance Assessment

August 29, 2016, 6:54 am

≫ Next: Factor Investing: Buyers Beware

≪ Previous: Most Useful Investment Blogs

(An earlier version of this article first appeared on the Alpha Architect blog.)

When I attended the Harvard Business School my favorite class was Managerial Economics. It focused on decision making under uncertainty [1].

The first thing to understand here is the concept of expected value. You determine this by multiplying each outcome by the probability of its occurrence, then adding them all together. For example, the expected value of a coin flip where you win $10 with heads and lose $5 with tails is (.5 * $10) + (.5 *-$5) = $2.50. We should be indifferent between playing this game and receiving $2.50 without doing the coin flip. In this case, $2.50 is both the expected value and the “certainty equivalent,” or what we would accept for certain instead of playing the game.

Three elements affect how we play the coin flipping game:

Risk aversion
Risk capacity
Risk tolerance

Risk Aversion

Let’s say we raise the stakes and with the same one-time coin flip we could win $10,000 with heads and lose $5000 with tails. Our expected value is $2500, but the amount we would accept for certain may now be different than $2500. Those who are risk seeking might play the game for an amount equal or greater to its expected value of$2500. Those who are risk averse wouldaccept less than $2500 instead of playing the game. Someone conservative, who does not like the idea of losing $5000 on a coin flip, might pay something to not have to play.

Risk Capacity

The amount of risk aversion we have depends on the size of the outcome relative to our financial condition. Because of risk aversion, we buy insurance having a negative expected value (and a positive one for the insurance company) in order to avoid the risk of catastrophic loss. On the other hand, risk seekers may buy low-cost lottery tickets with extremely negative expected values for the small chance of an enormous payoff. Thiscan be especially appealing to those having little to lose and much to gain.

Risk Tolerance

Risk tolerance defined by the ISO 22222 Personal Financial Planning Standards is “the extent to which a consumer is willing to risk experiencing a less favorable financial outcome in pursuit of a more favorable financial outcome.” It is an assessment of our psychological ability to deal with uncertain outcomes. It is not symmetric due to loss aversion. Investors will oftentrade $1.5 to $2 in gains to avoid $1 in losses [2].

Risk tolerance is generally a stable personality trait. But it is subject to situational influences, such as our mood, and may change due to our life experiences, such as aging.

Knowing our risk tolerance is important because financial decisions are motivated by emotional as well as logical factors. Investors, for example, often chase performance. They may invest based on attractive past results, then bail during periods of underperformance.

The 2016 annual Dalbar report showed the average U.S. equity fund investor earning 4.7% over the past 20 years, while the S&P 500 index gained 8.9%. Poor timing decisions caused nearly half of this underperformance. A dramatic case of this effect involved CGM Focus (CGMFX), the highest return U.S. stock fund from 2000 through 2010. It’s average annual return was 18.2%, but the fund’s typical shareholder lost 10% during that same period! Investors added heavily to this volatile fund near the top and bailed out as the fund neared its bottom.

When markets go up we may hop on board without considering the volatility that lies ahead. Risk tolerance assessment can help us avoid this behavior by showing us ahead of time our psychological ability to deal with uncertainty and risk. This can help us choose more suitable investments.

Recognizing that we are sometimes more emotional than rational, FINRA issued Regulatory Notice 12-25 in July 2012. It added risk tolerance to the list of factors that should be used to determine investment suitability. The other factors are age, financial condition, investment holdings, investment experience, time horizon, liquidity needs, tax status, and investment objective.

Current Practice

Yet many investment firms still use only traditional indicators of investor suitability that focus on the ability to absorb losses and on investment horizon. Fidelity, for example, asks new clients for the following information: investment purpose, time horizon, investment objective, annual earnings, net worth, liquid assets, investment experience, and liquidity needs.

Otherfirms try to integrate risk tolerance into their investor profile questionnaires. Vanguard, for example, added five risk tolerance questions to the other six questions in their client Investor Questionnaire [3]. Kudos to them for including a real world question of how you would (and did) react in 2008 when stocks lost 31% of their value. Our rational choices are not always the same as our emotional ones during times of actual market adversity.

I believe it is better to keep risk tolerance questions separate from questions like our time horizon, financial goals, and investment objectives. Risk tolerance and other investor profile questions should be evaluated separately to gain more insight into the differences between our financial goals and our behavioral biases. A robust risk tolerance questionnaire will tackle the behavioral elements of risk not covered by standard investor profile questions.

Risk Tolerance Questionnaire

A risk tolerance assessment can show us if our financial objectives are too conservative or too aggressive. Ignoring risk tolerance can cause us to abandon our financial plans during times of market stress. According to FinMetrica, 60% of the people who take FiMetrica's risk tolerance questionnaire (RTQ) find there is no strategy that will allow them to reach all their investment goals while adhering totheir risk tolerances. In such cases, investorsmight want to use their risk tolerance profiles to revise their financial goals.

What to Do

How do we go about using RTQs? In the 1980s, I developed my own. I asked investors to choose between various financial outcomes. From this information, I constructed their risk profiles. I was surprised to see how much variation there was in risk tolerance. It was then I realized this information could be useful for portfolio planning purposes.

The science of psychometrics, which is the blending of psychology with statistics, has evolved since that time. You no longer have to do all the work yourself. There are several services, like FinMetrica and Riskalyze, that offer RTQs to financial planners. There is also a freely available online RTQ by Ibbotson Associates and Financial Planning Services Australia.

In addition, John Grable and Ruth Lytton, two financial planning professors, have an RTQ you can access online. Several research papers document the validity of their questionnaire: Grabel and Lytton (1999) and Gilliam, Chatterjee, and Grabel (2010).

RTQ Issues

RTQs were criticized during the 2008 financial crisis for not anticipating how market turmoil could cause changes in risk tolerance. Critics argued that risk tolerance depends on market return and volatility. But Roszkowski and Davey (2010) present data collected pre- and post-crisis showing that the decline in risk tolerance was relatively small. What mostly changed was investors’ perception of risk.

The authors conclude that risk tolerance is a stable personality trait. Risk perception, however, changes because it is a cognitive appraisal of external conditions based on one's mental state. This is good news since risk perception can be modified through more information and better education.

Reevaluation

We cannot however look at risk tolerance just once and then forget about it. Risk tolerance does not take into account life changing events and shifting investment goals. We should periodically reevaluate risk tolerance, which is easy to do using the above tools.

Example of How to Use RTQs

I have three proprietary dual momentum models. I encourage investment professionals who license my models to use RTQs with their dual momentum clients. This can help them decide which model(s) best suit their investors' risk preferences while meeting their investment goals.

Other advisors should consider doing the same. If you manage your own account, you can follow the Greek maxim "Know Thyself" by using the RTQs by Ibbotson Associates or Grabel and Lytton. They can help you see if your investment portfolio is suited to your own risk tolerance and if, based on this, you should consider making some portfolio changes. Your financial and psychological health may depend on it.

[1] See Von Neumann and Morgenstern .

[2] See Tversky and Kahneman (1979).

[3] Another publicly accessible questionnaire that combines risk tolerance with other factors is in the Financial Planning Practitioner’s Guide by the Canadian Institute of Financial Planners.

↧

Factor Investing: Buyers Beware

September 16, 2016, 7:40 am

≫ Next: Book Review of Quantitative Momentum

≪ Previous: Risk Tolerance Assessment

A highlight of the 2016 Morningstar ETF Conference was the keynote address by the former leader of U.S. Navy Seal Team Six, Rob O’Neill. Chief O’Neill shared some stories about his training and operations as an elite Navy Seal. The take away lessons from his talk were the importance of preparation, discipline, and keeping the mission goal in mind. Overriding all this is the importance of tenacity. A Navy Seal survives eight months of insanely intense training by advancing one hour at a time without ever giving up.

Another important speaker at the event, Jason Hsu, showed that many professional investors do poorly because they lack this tenacity. They are instead influenced like the public by short term cyclical performance swings..

Investors often select investment managers or approaches based on 3 to 5 years of past performance. But 3 to 5 years is mean reverting with both markets and managers. Fired managers on average do 250 bps better than the new ones taking their place. Most investors, both professional and public, tend to be market timers whether they know it or not. And they are poor ones at that.

What we should do, according to Hsu, is stick with our long term goals and ignore shorter term cyclical performance swings. In other words, investors would do well to follow Chief O’Neill’s advice – prepare well, and stick to your plan with discipline and determination.

To proceed with confidence, we need to have a good understanding of the investment factors we are using. There has been abundant academic research on factors, beginning in the early 1990s with size and value. Factors in general have shown favorable results on paper. But now that factor-based investing has been around for a while, it might be useful to look at how factors have done on a real-time basis.

Out-of-Sample Factor Performance

McLean andPontiff (2015) looked at 97 factors from academic literature that predicted cross-sectional stock returns. They found an average decay of 32% in factor returns following their publication. Calluzo,Moneta, and Topaloglu (2016) looked at 14 well-documented anomalies from 1982 through 2014. They included value, momentum, profitability, and investment These authors also found a 32% decay in average returns post-publication.

Glushkov(2015) examined a comprehensive sample of 164 domestic equities smart beta (SB) ETFs from 2003 through 2014. The factors examined were size, value, momentum, quality, beta, and volatility. Glushkov concluded, “I found no conclusive empirical evidence to support the hypothesis that SB ETFs outperformed their risk-adjusted benchmarks over the studied period.”

Yet factor based investing has been growing in popularity. The emphasis of the Morningstar ETF Conference was factor investing, and Conference sponsors were busy promoting factor-based ETF products.

The Conference set the tone for this with an early talk by Ronen Israel of AQR that featured the two most popular factors, value and momentum. Israel pointed out momentum’s tax efficiency and how it can help offset value traps in a diversified value and momentum portfolio.

Momentum Issues

One of the issues associated with stock momentum is price impact due to scalability limits. Momentum performs substantially better with focused portfolios of 100 or fewer stocks and with frequent rebalancing. Unlike value, momentum is a high turnover strategy. If you turn over 30% of a 100 stock momentum portfolio each quarter, it does not take many billions of dollars to have a large impact on price. Israel did not address this issue, but his firm, AQR Capital, is not ignorant of this fact. AQR has held an average of more than 400 stocks in its U.S. large cap momentum fund portfolio.

Momentum stocks are also volatile with wide bid-ask spreads. This volatility contributes to their higher transaction costs. Israel pointed out a study by Lesmond et al(2004) in which transaction costs completely offset the profits of momentum investing. Israel then pointed to a proprietary 15-year data set showing momentum portfolios earning decent profits at the cost of more tracking error. But a recent study by Fisher, Shah,and Titman (2015) using observed momentum stock bid-ask spreads found transaction costs to be higher than Israel’s figures and closer to Lesmond’s.

Momentum Performance

Let us take a look then at the performance of the two oldest rules-based momentum funds. They are the PowerShares DWA Momentum ETF (PDP) that began in March 2007 and the AQR U.S. Large Cap Momentum Style Fund (AMOMX) that started in July 2009. Both funds have underperformed their benchmark from their beginning until now.

Value Investing

Let us move on to value, which is the most popular investment factor. Of the 8000 or so U.S. mutual funds, more than 1000 are value funds. Value is the only factor that appears in every multi-factor ETF.

Israel showed that value is best determined using a combination of multiple valuation methods. All metrics performed about the same over the long run, but performance varies considerably over time. Of five different value metrics, earnings-to-price (E/P) was best overall, but it was the top metric in only 2 out of 6 decades.

The value premium has been insignificant among U.S. large cap stocks [1]. But Israel pointed out that value can still be useful when combined with momentum. According to Israel, value should make up one-third of a combined value and momentum portfolio, even if value has zero expected return. This is because value can reduce the volatility and tracking error of a momentum portfolio. But diversification this way can create considerable performance drag. In our Morningstar Conference breakout session on momentum, Wes Gray, Meb Faber, and I described how trend following could create a reduction in risk exposure without this kind of performance drag.

Value Performance

As we did with momentum, let us see now how value funds have performed real time. Using the CRSP database, Loughran and Houge(2006) looked at the performance of U.S. equity funds from 1962 through 2001. They used the prior 36 months to sort funds by style and size. From 1965 through 2001, the average large cap growth fund returned 11.3% annually, while the average large cap value fund returned 11.41%. The outperformance of 0.11% for value over growth was insignificant.

For small caps, where value is said to have a greater advantage over growth, the authors’ results showed the opposite to be true. Small cap value funds earned 14.10%, while small cap growth funds returned 14.52%. Small cap value underperformedsmall cap growth by 0.42% per year. The authors say that bid-ask spreads, transaction costs, and the price impact of trading likely work against the capture of value premium in small-cap stocks. These are the same issues that concern us with respect to stock momentum. The authors conclude, “We propose that the value premium is simply beyond reach…investors should harbor no illusion that pursuit of a value style will generate superior long-run performance.” [2]

Source: Loghran and Houge (2006), “Do Investors Capture the Value Premium”

I was curious about the performance of value versus growth since 2001. I also wanted to see value versus growth for index funds rather than for all funds. Fortunately, Vanguard opened their value and growth index mutual funds in November 1992. The higher blue line in the chart below is the performance of the Vanguard Index Trust Growth Index Fund (VIGRX). The lower black line is the Vanguard Index Trust Value Index Fund (VIVAX).

Past performance is no assurance of future results.

Growth has outperformed value here by 0.6% per year since the end of 1992. In summary, with fund data from 1965 until now, value has shown no significant advantage over growth on a real time basis.

Real World Versus Academic World

Everyone likes the idea of value investing. We are used to finding bargains and buying what is cheap. But value stocks may look cheap for a reason. Horrendous tracking error and lower than expected real time returns may make them less appealing. Perhaps Fama and French were on to something when they omitted both momentum and value from their latest factor pricing model.

The Capital Asset Pricing Model (CAPM), Mean-Variance Optimization (MVO), and Portfolio Insurance were all elegant academic concepts that looked great on paper, but never held up in the real world. Maybe factor-based stock investing will suffer the same fate.

[1] See Asness et al.(2015).

[2] The median expense ratio for growth funds was 11 basis points higher than for value funds. Since growth funds also realized slightly higher average returns, expense ratios cannot explain the absence of a value premium across mutual fund styles.

↧

Book Review of Quantitative Momentum

October 17, 2016, 1:48 pm

≫ Next: Common Mistakes of Momentum Investors

≪ Previous: Factor Investing: Buyers Beware

I have been looking forward to Wes Gray and Jack Vogel's new book, Quantitative Momentum.

It is the only book besides my own Dual Momentum that relies on academic research to develop systematic momentum strategies. My book uses a macro approach of applying momentum to indices and asset classes. Wes and Jack (W&J) take a more common approach and apply momentum to individual stocks.

W&J begin their book with an excellent question. Since there is ample research showing momentum to be a superior investment approach over the past 200 years, why isn’t everyone using it?

W&J do good job explaining the behavioral biases that keep many investors away from momentum. W&J also discuss marketplace constraints like advisor career risk when momentum underperforms its benchmark.

But these biases and constraints are not undesirable if one becomes a momentum investor. They keep momentum from being over exploited.

In Chapter 1 W&J give a short history of trend based versus fundamental analysis based investing. They show that both approaches can work.

In Chapter 2 W&J discuss irrational-noise traders who can dislocate prices from their fundamental values and keep them dislocated for some time. In the case of value, investors overreact in the short-run to bad news. In the case of momentum, investors under react to good news.

Investment managers are hired to exploit long-run profit opportunities, but their performance is judged by investors looking at short-term results. Advisors who continue to focus on longer-term opportunities, like value or momentum, may then get fired. So anomalies like momentum do not get arbitraged away. Mispricing can persist, sometimes for long periods.

In one of the key points of the book, W&J discuss the importance of sustainable investors as well as sustainable alpha. Gregg Fisher once said, “We don’t have people with investment problems. We have investments with people problems.” Investors often lack the requisite patience to stay with their chosen strategies during periods of benchmark underperformance.

To prepare investors for those difficult times, W&J highlight the risks associated with value and momentum investing. They point to Julian Robertson’s Tiger Funds. These funds lost almost all their clients by sticking to their value model in the late 1990s. Value underperformed the market in 5 out of 6 years then, sometimes by double digits. W&J made this surprising statement, “True value investing is almost impossible.”

What can investors do about this? W&J point out that momentum is largely uncorrelated with value. This means an investment in momentum can make value investing more tolerable. Investors should keep in mind though that momentum and value are largely uncorrelated only when their market risks are hedged. As long-only strategies, momentum and value are correlated to the market and to each other. All can simultaneously experience large bear market losses.

There is another reason why non-correlation may not be so important. As I show in my recent blog post, “Factor Investing: Buyer Beware,” value investing in actual practice has not shown any significant advantage over the market. If investors have no reason to hold value stocks, momentum loses some of its attractiveness as a diversification strategy.

In Chapter 3 W&J give a brief history of stock market momentum and the important psychological challenges of momentum investing. W&J make the point that value and momentum are similar since both are driven by short-term investor pessimism. With value, pessimism is because of poor fundamentals. With momentum, pessimism is about performance continuation.

W&J reveal that momentum, like value, can underperform over long periods. They point out a 5-year stretch when momentum underperformed the broad market by 15%. This could be challenging to any investor.

In Chapter 4 W&J show that a 50/50 allocation to value and momentum can reduce the tracking error of separate value and momentum portfolios during extended periods of relative poor performance. What is also worth noting is the decline over time of both value and momentum premia.

In Chapter 5 W&J show momentum as an intermediate term anomaly. Stocks exhibit a strong reversal pattern in returns when performance is measured over a short period of 1 month or less. There is also mean reversion of returns on a long-term basis of 3 to 5-years.

Stock momentum works best using an intermediate term 3 to 12-month look back period. W&J use 12-months for quantitative momentum and skip the most recent month because of mean reversion.

W&J show in Table 5.5 that frequently rebalanced, concentrated momentum portfolios perform best. Ideal portfolios hold only 50 stocks and get rebalanced monthly.

W&J point out that concentrated portfolio/higher rebalance frequency is not a good approach for large asset managers with billions to invest because of scalability issues.

Stock momentum is a high turnover strategy, and many momentum stocks are volatile with wide bid-ask spreads. There is bound to be some price impact from trading momentum stocks. This is especially true with frequently balanced, concentrated momentum portfolios.

Everyone now knows the top-ranked momentum stocks. In fact, Alpha Architect shows the top 100 momentum stocks on their website each month to anyone who registers there. So all investors, not just multi-billion-dollar asset managers, may experience adverse price impact from trading the same momentum stocks that everyone else does.

Transaction costs can be a similar problem. W&J mention a paper called "The Illusionary Nature of Momentum Profits" by Lesmond, Schill, and Zhou (2002) published in the Journal of Financial Economics. Lesmond et al. conclude that after transaction costs, momentum profits are largely illusionary. W&J also mention research by Korajczyk and Sadka (2004) showing that stock momentum has a limited capacity of only about $5 billion.

Offsetting these arguments, W&J present the findings of Frazzini, Israel, and Moskowitz (2014) of AQR. Frazzini et al. argue that momentum trading costs are manageable based on AQR’s own proprietary transaction data from 1998 through 2011.

But the amount of capital in momentum strategies is higher now. In a more recent paper, Fisher, Shah, and Titman (2015) use observed bid-ask spreads from 2000 through 2013. They report, “Our estimates of trading costs are generally much larger than those reported in Frazzini, Israel and Moskowitz (2012), and somewhat smaller than those described in Lesmond, Schill and Zhou (2004) and Korajczyk and Sadka (2004).” Research by Jason Hsu PhD, co-founder of Research Affiliates, also supports the higher transaction cost conclusions of Fisher et al. and Lesmond et al.

Scalability and transaction costs are reasons why we prefer to use momentum with indices and asset classes rather than with individual stocks. Another reason is that according to Geczy and Samonov (2015), momentum applied to stock indices outperforms momentum applied to stocks even before transaction costs.

Chapter 6 is where W&J explain path dependency is and why it matters. They cite research by Da, Gurun, and Waracha (2014) showing that smooth and steady past performance is preferable to jumpy performance. The underlying logic is that investors underreact to continuous information. Investors should therefore prefer momentum accompanied by steady price appreciation rather than discreet price jumps.

To implement this idea, W&J advocate double sorting stocks on their 12-month momentum and their percentage of positive daily returns over the past 252 trading days. What they call “high-quality momentum" are top decile momentum stocks with the largest percentage of positive daily returns. Results are from 1927 through 2014. Transaction costs are not included.

The improvement in high-quality over generic momentum looks good. But a possible warning sign is W&J’s statement at the beginning of Chapter 6: “For over a year, we examined every respectable piece on momentum stock selection strategies we could find…”

Extensive data mining increases the odds that favorable results may be due to chance. Say you have different studies each showing no significance with a 95% confidence level of being correct. If you examine 20 or more of these studies, there is a good chance that one of them will be look significant even though the chance of that being correct is still only 5%. The classic green jelly bean example should make this clear.

Source: http://xkcd.com/882

In Chapter 7 W&J attempt to further enhance momentum by adding seasonality. In the turn-of-the-year or January effect, investors engage in year-end tax loss selling. They hold on to their strongest stocks and may buy more of these as replacements for the stocks they sell.

Window dressing to make their quarter-end portfolios look more attractive may also cause investment professionals to sell their losers and buy more winners before the end of the quarter. To take advantage of these seasonal tendencies, W&J advocate rebalancing their momentum portfolios at the end of February, May, August and November. They say this may help capture higher momentum profits during the months following the end of each calendar quarter.

Here are the results from incorporating seasonality as “smart rebalancing.”

There is little risk-adjusted improvement over agnostic (generic) momentum as seen from the increase of only .01 in the Sharpe and Sortino ratios. But since portfolios are rebalanced quarterly anyway, there should be no harm in picking non-calendar ending quarters for doing so.

In Chapter 8 W&J suggest that readers address the trading cost issue by comparing the analysis presented in Lesmond et al. and Frazzini et al. They do not mention here the more recent studies by Fisher et al. and Hsu that I discuss above.

W&J then do an in-depth analysis of “quantitative momentum” with respect to reward, risk, and robustness. They finish the chapter by making the point that momentum is sustainable because investors will continue to have behavioral biases. Investors are short-sighted performance chasers. This should also keep them from overexploiting the anomaly.

In the words of W&J, “… strategies like value and momentum presumably will continue to work because they sometimes fail spectacularly relative to passive benchmarks.” This may not be good news for those who at that time own momentum or value stocks. But W&J offer these words of encouragement. “The ability to stay disciplined to a process is arguably the most important aspect of being a successful investor” (emphasis added).

In Chapter 9 W&J show a combined 50/50 allocation to an equal weight, quarterly rebalanced momentum and value portfolio from 1974 through 2014.

The combined portfolio return is higher than that of momentum or value on their own. The combined portfolio also has less tracking error than either momentum or value vis-a-vis the broad market. Combining value and momentum shortens both the length and depth of benchmark underperformance.

As a final tweak to their approach, W&J show a trend following overlay applied to the combined portfolio. If a 12-month moving average of the S&P 500 index is greater than zero, they hold the combined portfolio. If the moving average is less than zero, they hold Treasury bills. Using this trend filter, the worst drawdown of the combined approach goes from -60.2% to -26.2%. But investors give up 1.5% in compound annual return, and there is an increase in tracking error.

My research shows that trend-following is more effective when applied to stock indices rather than to portfolios of individual value or momentum stocks. The reason for this has to do with volatility. The standard deviation of W&J’s quantitative momentum and combined portfolios are 25.6% and 21.4%. The standard deviation of the S&P 500 index is 15.5%. Higher volatility means you give up more profit before you can exit or re-enter stocks when using a trend following filter.

W&J finish the book by again referring to relative performance risk. One cannot stress enough that myopic investors give up potentially superior results when they become nervous or impatient and abandon their strategies.

In an Appendix, W&J examine some possible enhancements to momentum. These include earnings momentum, proximity to 52-week highs, stop losses, and absolute strength. Although W&J use the terms interchangeably here, you should not confuse absolute strength with absolute momentum. Otherwise, their analysis here is first rate. I would like to see W&J apply the same degree of analytic rigor to strategies that are sometimes just quickly presented on their Alpha Architect blog.

I remain skeptical about momentum applied to stock portfolios. Macro momentum applied to stock indices is a simpler approach that shows the same high potential returns as “quantitative momentum.” Macro momentum with broad-based indices has substantially lower transaction costs and no scalability issues. It also responds better to trend-following.

But I still recommend Quantitative Momentum to momentum investors for the following reasons:
1)    Its emphasis on sustainable investors who can keep the big picture in mind and not be swayed by short-term performance
2)    Its review of momentum principles and behavioral biases
3)    Its rigorous research in the book’s Appendix

↧

Common Mistakes of Momentum Investors

November 30, 2016, 12:37 am

≫ Next: Are Commodities Still a Good Portfolio Diversifier?

≪ Previous: Book Review of Quantitative Momentum

Like most investors, those using momentum are often guilty of chasing performance. In fact, momentum requires that we do this. But it should be done in a disciplined and systematic way. Performance chasing should not be due to myopia, irrational loss aversion, or other psychological biases.

Behavioral Challenges

It is not always easy adhering to a disciplined approach. If you are not vigilant, emotions can get the better of you. Even Daniel Kahneman, the father of behavioral economics, admits to being influenced by behavioral heuristics.

We may forget our strategy’s long-term expected outperformance when we experience uncomfortable drawdowns. The survival instinct kicks in strongly then. Recency bias can make us feel the drawdown will never end.

We may also have to deal with regret aversion when our portfolio underperforms. This will happen sooner or later. No strategy outperforms all the time. Occasional benchmark underperformance is the price we pay for possible protection from severe bear markets.

Myopia

Those who look at performance frequently do not do as well as those who are less concerned with short-term performance. When someone asks me how my models are doing this year, I know they do not have a good understanding of momentum being a long-term approach. Last May a dual momentum investor sent me an email saying his wife’s account in REITs was outperforming his momentum account. He then closed his account and invested in REITS himself. Since then, REITs have declined more than 10%, while momentum has gone up almost the same amount. This scenario has happened more frequently than you might think.

It is important to keep the big picture in mind. We should wait at least a full bull and bear market cycle before evaluating the performance of a dual momentum strategy. Do your homework so you understand whatever investment approach you select. Then relax, and enjoy the journey.

Accepting Lower Risk Premia

The other serious mistake momentum and other investors make is not understanding the real goal of investing. We should invest in a way that offers us the highest expected return while limiting our risk exposure. Limiting downside exposure is important so we do not panic under stress and do stupid things.

The stock market has had two bear markets over the past 20 years. Each time stocks lost more than half their value. Because of this, investors have been extra cautious. Many have tried to use broad diversification to reduce their portfolios' drawdown exposure.

If you select non-correlated assets, you can achieve some reduction in volatility and drawdown. But your expected return is the weighted average return of all your assets. That is where the problem lies. Assets with lower expected returns will reduce your portfolio's return.

Bonds have done well over the past 15 years. But longer term, their real return is less than one-third the real return of stocks. Given how low interest rates are now, there is not much room for bonds to appreciate further. In fact, current interest rates predict low bond returns in the years ahead.

Bonds are also not as low-risk as you might think. Since 1900, the worst real return drawdown was 73% for stocks and 68% for bonds. As we see below, stocks and bonds can sometimes have severe drawdowns simultaneously.

Bonds not only create a drag on our performance. They also may not reduce our risk exposure when we most need them to do so.

Some advisors recommend alternative assets, like commodities, with little or no expected real return. This is because such assets are generally (but not always) less correlated to equities. They can therefore reduce portfolio volatility. But the addition of low-return alternative assets can create an even more serious drag on portfolio performance.

What momentum investors should remember is absolute momentum does a much better job of reducing drawdown. Because of this, trend following absolute momentum lets us keep more of our assets in equities where we can receive more risk-premium.

Using Stocks and Sectors

My first research paper released in 2011 analyzed equity momentum with individual stocks, sectors, style attributes, and regions. I showed that momentum works best when applied to geographically diversified equity indices. Last year Geczy and Samonov (2015) applied momentum to stocks, stock sectors, geographic equity indices, bonds, commodities, and currencies. They also found equity indices performed best. This is without considering the issues of scalability and trading costs associated with individual stocks. (See my last blog post for more on this). Yet most articles about momentum and most momentum funds still use stocks instead of stock indices. Broadly diversified, low cost stock indices do not get the respect they deserve.

Some momentum investors still adhere to the old paradigm of extensive diversification. They hold more assets than they need for optimal portfolio growth. I posted an article and mentioned on my website’s FAQ page that the long-run performance of sector rotation is not as good as momentum with broad stock indices. But I still get plenty of emails asking me about sector rotation and the use of other higher risk or lower return assets.

Preference for Complexity

Investors and advisors seem to prefer complexity over simplicity. Many must believe that elaborate models and more diversified portfolios perform better than simpler approaches. My research shows this is not the case. I tried adding factor-based indices and additional asset classes to my dual momentum models. My models worked best using just broad-based indices for U.S. stocks, non-U.S. stocks, and short or intermediate bonds.

Advisors may prefer complexity to justify their fees. It could be challenging to charge fees for putting clients in an S&P 500 index fund. Robo-advisors are the latest slice and dice diversification strategy for those who think more is better.

Non-Optimal Portfolio Construction

Some portfolios suffer because investors rely on well-known measures like the Sharpe ratio for selecting assets. The Sharpe ratio divides excess returns by the standard deviation of those returns. It is an efficiency measure telling you how much return you might expect per unit of volatility. But unless returns are normally distributed (they almost never are), the Sharpe ratio is not a good indicator of tail risk. Nor is it a good indicator of the amount of wealth you might accumulate or your chance of future investment success.[1]

Wiecki et al. (2016) looked at 818 algorithmic trading strategies at Quantopian, a research boutique. Using data from 2010 through 2015, they found that the Sharpe ratio offered little value in predicting out-of-sample performance. This was also true of similar metrics such as the information ratio, Sortino ratio, and Calmar ratio.

You can increase the Sharpe ratio of most portfolios by simply adding more bonds. But your expected rate of return and accumulated wealth will in most cases suffer.

Here is an example showing the performance of the S&P 500 index compared to a balanced portfolio with 60% in the S&P 500 index and 40% in the Barclays Capital U.S. aggregate bond index. The data is from the start of the bond index in January 1976 until November 2016. It represents a possible 40 year holding period of someone saving for retirement.

	S&P 500	60/40
CAGR	11.6%	10.4%
Standard Deviation	14.8%	9.6%
Sharpe Ratio	0.43	0.49
Worst Drawdown	-50.9%	-32.5%
$10,000 Grows to	$781,760	$507,070

Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Please see our Disclaimer page for more information.

The difference in annual return of 1.2% gives a 54% increase in ending wealth over this 40 year span. Which portfolio would you rather have? In this example, the most desirable portfolio may depend on your on your risk tolerance. The 60/40 portfolio has a less painful worst drawdown.

Here are the results adding the simple Global Equities Momentum (GEM) model featured in my book and in an earlier blog post. GEM uses relative momentum to switch between U.S. and non-U.S. stock indices, and absolute momentum to switch into aggregate bonds when stocks are weak. GEM’s single parameter, the look back period, was discovered in 1937. GEM uses a combination of relative and absolute momentum. Both have shown good results on over 200 years of back data.[2]

	S&P 500	60/40	GEM
CAGR	11.6%	10.4%	17.1%
Standard Deviation	14.8%	9.6%	12.5%
Sharpe Ratio	0.43	0.49	0.87
Worst Drawdown	-50.9%	-32.5%	-17.8%
$10,000 Grows to	$781,760	$507,070	$5,416,080

Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Please see our Disclaimer page for more information.

Which portfolio would you now choose? How difficult would it be for you to live with GEM?[3]

When I analyze investment opportunities, my primary criteria are a high CAGR combined with a tolerable level of risk exposure. CAGR represents the geometric growth rate of one’s capital.[4] It takes volatility into account. If two strategies have the same average return, the one with lower volatility will have a higher CAGR.

But like the Sharpe ratio, CAGR does not measure tail risk. Extreme downside exposure can cause you to exit positions prematurely screaming in pain or cursing your investment advisor. That is why I also consider drawdown.

Worst drawdown is only a single point in time, but it can give you a pretty good idea about tail risk. I also examine the distribution of returns and look at all the other drawdowns. Keep in mind that your worst drawdown may lie ahead still. Having a simple, robust approach that performs well over a long period may reduce that risk.

Impatience

It is important to remain focused on what is important – accumulating wealth while protecting yourself from severe bear markets. Once you have a good investment strategy, you need to be patient so it can do its work for you. Warren Buffett said the stock market is a mechanism for transferring wealth from the impatient to the patient. This applies to momentum as well as other investors.

[1] See Levy (2016).
[2] See Geczy and Samonov (2015).
[3] I also have an enhanced version of GEM that I license to a few investment professionals.
[4] For econ geeks, CAGR is consistent with logarithmic utility. The Sharpe ratio represents quadratic utility, unless returns are normally distributed. See Friedman and Sandow (2004) and Levy (2016).

↧

Are Commodities Still a Good Portfolio Diversifier?

January 2, 2017, 6:46 pm

≫ Next: Factor Zoo or Unicorn Ranch?

≪ Previous: Common Mistakes of Momentum Investors

Overfitting the data is a serious problem when constructing financial models. One way to guard against this is to have lots of data. This helps you determine if your results are robust by seeing how they hold up over different time periods.

But this assumes the underlying market dynamics remain stable over time. That is not always the case. Gogi Gerwal gives a good example of how you may be misled by extrapolating past results to the future. In his blog post, “Should We Consider Gold?” Gogi showed that adding gold to the Global Equities Momentum (GEM) model increased its annual return from 18% to 21%.

Source: SharpeReturns.ca

Gogi then pointed out that from 1971 to 1981 gold’s price went up 18-fold when the U.S. let the price rise to market levels. If we remove this period of unnatural price appreciation from the back test, GEM without gold has a higher return and less volatility.

This is an example of aggregation bias. By combining events that are different into one set of data, your results may look good. But appearances can be deceiving. Much different market forces at work on different parts of the data means you should consider the different periods separately. Commodity futures are another area where people may be misled using aggregated data.

Here is a long-term perspective on commodity performance versus stocks and bonds:

The blue line represents a traditional 60/40 balanced stock and bond portfolio. The green line is the popular S&P Goldman Sachs Commodity Index (GSCI) of commodity futures contracts. S&P GSCI had strong but erratic performance until mid-2008 when it collapsed.

There has been a strong disconnect in commodity index performance before and after 2008. We should see if the more recent performance is a normal variation or if it signals a change in market dynamics. This is important to know because many institutional investors use commodities to diversify their investment holdings. The Chartered Financial Analyst (CFA) curriculum recommends a 10% commodities allocation to a typical stock and bond portfolio.

Commodities Diversification

The idea of diversifying stocks and bonds with commodities first began with Gorton and Rouwenhorst (2005) in their paper, "Facts and Fantasies about Commodity Futures." They showed that from 1959 through 2004, a collateralized basket of 36 commodity futures had equity-like returns of 11.2% per year, while being negatively correlated to stocks. This meant a combination of commodities and equities could have large diversification benefits. In a 2006 report called "Strategic Asset Allocation and Commodities" commissioned by PIMCO, Ibbotson Associates also issued a report showing that commodities could be a valuable addition to traditional stock and bond portfolios.

Growth of Commodities

Following these two studies, institutional interest in passive, long-only commodity exposure skyrocketed. Goldman Sachs, PIMCO, and others aggressively marketed commodity index products. Many pension funds entered the market. Long positions in commodity indices went from $6 billion in 1999 to $256 billion by mid-2008. Annualized growth among the S&P GSCI commodities averaged 31% during the 2004-2006 period. This rate was nearly triple that of 2001-2003. Market participation by non-commercial traders tripled from 15% in 1990 to 42% in 2012. [1]

Despite poor performance since 2008, demand for commodities has remained strong. Commodity investments more than doubled from roughly $170 billion in July 2007 to $410 billion in February 2013. According to Bhardwaj, Gorton, and Rouwenhorst (2014) in their paper, "Facts and Fantasies About Commodity Futures Ten Years Later" open interest of the average commodity contract has more than doubled since 2004. Endowments, pension funds, hedge funds, and the public have all joined the bandwagon by adding commodity index futures to their portfolios. Following good performance in the 1980s and 1990s, speculative demand for commodities has also grown in the managed futures industry. Barclays Hedge reported that the amount in managed futures went from $50.9 billion in 2007 to $325 billion in 2014.

Financialization of Commodities

Increased participation of fund managers, pension plans and other financial investors created what some call the financialization of commodities. Under financialization, commodities are influenced by the aggregate risk appetite for financial assets and the investment behavior of commodity index investors. These investors have less commodity specific knowledge and a different attitude than commercial interests.

Financial investors enter or exit trades based on their perception of the macroeconomic situation, rather than on market specific fundamental factors. Increases in the supply of price insurance by financial investors has lowered the price hedgers pay for protection.

Financial investors improve the sharing of commodity price risk. Financial investors also channel volatility from outside markets to the commodity markets.

Source: Zaremba (2015),"Is Financialization Killing Commodity Investments?"

Increased Correlations

Financialization has also had an impact on the correlation of commodities to other assets. In their paper, "Financialization, Crisis and Commodity Correlation Dynamics," Silvennoinen and Thorp (2009) report on the conditional volatility and correlation dynamics of commodity futures from May 1990 until July 2009. They found increasing integration between the commodity and financial markets. Hedge fund managers were timing their futures exposure for hedging purposes.

Financialization means that returns from both commodity futures and stocks decrease in volatile markets. During the 2008 financial crises, the correlation between stocks and commodities shot up to over 0.80. Increasing correlation during times of financial stress diminishes the diversification value of commodities. Cheung and Miu (2010) also show in their paper "Diversification Benefits of Commodity Futures," that commodities are not a good diversifier in bearish equity environments. Bhardwaj et al. say that correlations increase in periods of market turmoil. Commodities are often acquired for their hedge-type protection during bear markets in stocks. But we see here that this kind of protection may no longer exist.

Commodities now may also be more correlated in general with other assets. The table below from Bhardwaj et al. shows the one-year correlation of commodity futures with stocks went from -0.10 in July 1959 through Dec 2004. to 0.60 from Jan 2005 through Dec 2014.

In their paper "Correlation in Commodity Futures and Equity Markets Around the World: Long-Run Trend and Short-Run Fluctuation," Li, Zhang, and Du (2011) looked at dynamic conditional correlations (DCC) from 2000 through 2010 between 45 country equity markets and the S&P GSCI index. DCC preserves trends without smoothing fluctuations. Using DCC, the authors concluded, “… we are able to decisively disapprove the assertion that, despite recent history, commodities still provide portfolio diversification. Whether from the long-run or short-run perspective, the diversification value of the commodity futures index has, in general, vanished.” Li et al. attribute this to an increase in the integration between commodity futures and equity markets, and an increasing number of investors holding both commodity futures and equities.

Commodities may now also be more correlated to each other and to commodity indices, as we see from the Bhardwaj et al. study:.

In an interesting paper called "The Strategic and Tactical Value of Commodity Futures," Erb and Harvey (2006) show that the average annualized excess return for individual commodity futures from 1945 through 2004 was near zero. Portfolio returns of more than 10% came from mean reversion profits through portfolio rebalancing. Since correlations were so low back then, the diversification premium was enormous.

Lower Profits

If Erb and Harvey are correct and intra-commodity correlations are higher now due to financialization, then we should see lower commodity portfolio returns. We should also see higher volatility. This is because commodities are bought and sold at the same time by financial investors rather than fluctuating independently based on their individual fundamentals.

There may be another reason why commodity futures returns are lower now. To see why, we need to understand how the futures markets work. Alpha Architect had a good overview of that last week.

In brief, there are three components of commodity futures return. They are the return from holding Treasury bills as collateral, spot return from changes in commodity prices, and the yield associated with rolling over futures contracts.

Importance of Roll Yield

According to Campbell & Company (2014) in "Deconstructing Futures Returns: The Role of Roll Yield", the cumulative impact of roll yield can be significant. In some cases, it is similar in size to the entire gain or loss an investor experiences over the lifetime of a trade. Erb and Harvey (2006) reported that from December 1982 to May 2004, roll returns explained 91% of the expected long-run cross sectional variation of commodity futures excess return. According to Anson (1998) in"Spot Returns, Roll Yield, and Diversification with Commodity Futures," roll yield provided most of commodity investments’ total excess return between 1982 and 1997. The S&P GSCI average annual roll yield then was 6.1%, while the average spot return was -.08%.

What happened to roll yield since financialization of commodities began? In the chart below, the difference between the commodities return and the commodity futures return is the roll yield.

Bhardwaj et al. show that the much of the reduction in commodity futures return in recent years is due to a lower collateral returns. But excess futures returns (futures returns less U.S. Treasury bill returns), have declined from 5.23% annually in 1959 through 2004 to 3.67% in 2005 through 2014 on an equal weight portfolio. This is a 30% reduction in roll yield (risk premium). What is also important is the 26% increase in standard deviation from 12.1% in the earlier period to 15.23% in yhe later one. Return, volatility, and correlation are all are used in determining optimal portfolios.

Here is an explanation for why the risk premium has diminished. Hedgers can be on either side of a commodity market. When raw material prices go up, consumers still need to heat their homes, drive to work, and feed their families. Builders still need to buy lumber. There are no good substitutes for these things. This means the price elasticity of demand is generally low for commodity end products. Commercial interests who need to buy commodities can often pass price increases on to consumers rather than hedge future supply costs in the futures markets.

But the situation is different for commodity producers, such as farmers, mining and energy producers. They need to accept whatever the market prices is once their products are produced. To avoid the risk of not being able to cover their production costs, producers will hedge their price risk. They do this by selling futures contracts ahead of production that guarantees them a known selling price. Hedgers are willing to pay a premium to lay off this price risk. They offer speculators who take the other sides of their trades a positive return. More speculative activity now means less risk premium and lower roll yields.

In "Systematic Risk, Hedging Pressure, and Risk Premiums in Futures Markets," Bessembinder (1992) found that from 1967 through 1989, the average return of 16 non-financial futures was influenced by the degree of net hedging. Commodities in which hedgers were net short had positive excess returns for speculators to capture. When long-only financial investors entered these markets in force, the risk premiums they received from hedgers had to be spread out among many more participants.

The reduction in risk premium also helps explain the declining performance of commodity trading advisors (CTAs) in recent years. There has been less hedger premium available to them as well. In their paper, "Fooling Some of the People All of the Time: The Inefficient Performance and Persistence of Commodity Trading Advisors," Bhardwaj, Gorton, and Rouwenhorst (2013) reported that CTA excess returns over U.S. Treasury bills averaged only 1.8% from 1994 through 2102. This is not statistically different from zero.

Optimal Portfolios

With the trend toward higher correlations, higher volatility, and lower risk premiums, we should look again to see if commodity futures can still add value to a stock and bond portfolio. Besdies Bhardwaj et al. (2014), there have been three other academic studies addressing this question over the past five years. They all use more data from outside the financialization period than from within it. So their results are not overly biased toward recent events.

The first study was "Should Investors Include Commodities in Their Portfolios After All? New Evidence," by Daskalaki and Skiadopolous (2011). They looked at the S&P GSCI and DJ-UBSCI (now Bloomberg Commodity Index) from 1989 through 2009. Only one-quarter of their 20- year sample period was post-financialization. The authors did a portfolio spanning analysis. This shows what happens to the efficient frontier of optimal portfolios when you add more assets. They found that when you take higher order moments of the portfolio return distribution into account, commodities were not beneficial. When using rolling returns to construct out-of-sample data, the authors discovered that commodities were not beneficial even to mean variance investors.

The second study was "On the Correlation between Commodity and Equity Returns: Implications for Portfolio Allocation," by Lombardi and Ravazzolo (2013) of the Bank for International Settlements. They applied time-varying DCC correlations to the S&P GSCI and the MSCI Global Equities indices from 1980 through 2012. Again, only one-quarter of the data was post-financialization. These authors found the inclusion of commodities boosted returns for horizons of 2 to 4 weeks but at a cost of substantially higher volatility. They concluded that the idea of including commodities in one’s portfolio as a hedging device is not grounded.

The final study was "Portfolio Diversification with Commodities in Times of Financialization,"
by Zaremba (2015). He used spanning tests on the JP Morgan Commodity Curve Index data combined with stocks and bonds from 1991 through 2012. His conclusion was also that including commodity futures in a traditional stock and bond portfolio was no longer reasonable.

Front Running

There are a few more things that commodity index investors should be aware of. The first is a study by Mou (2011) called "Limits to Arbitrage and Commodity Index Investment: Front-Running the Goldman Roll." Mou examined the costs to investors of front running. This occurs when hedge funds or others buy the next month futures contracts just ahead of their usual rollover dates. Front runners then unwind their positions after prices have been pushed higher by index managers who bought the new contracts. Mou estimated that front running the S&P GSCI from January 2000 to March 2010 cost S&P GSCI index investors 3.6% in annual return. The popular S&P GSCI and Bloomberg commodity indices both use fixed rollover dates. Front running takes a toll on the performance of both indices, as well as on any funds using those indices.

Index Versus Fund Performance

One should also consider the costs associated with being in commodity index funds. I calculated the difference between index and fund returns since the start of the two oldest commodity index exchange-traded funds. The iShares S&P GSCI Commodity Indexed Trust (GSG) underperformed its index by 87 basis points per year. The iPath Bloomberg Commodity Total Return ETN (DJP) underperformed its index by 106 basis points. The annual expense ratio of both funds is 75 basis points. This accounts for some, but not all, of the difference in performance between the funds and the indices. None of the above portfolio optimization studies take these significant costs into account.

Weight of Evidence

Despite public information about lower roll yields, changing correlations, front running costs, and index expenses, researchers like Levine, Ooi, and Richardson (2016) are still positive about using commodity index futures as a portfolio diversifier. Their paper, "Commodities for the Long Run," does not discuss financialization. Instead, it uses a large amount of data to point out that commodity returns are sensitive to business cycle changes and the rate of inflation.

Here is a chart from Bhardwaj et al. (2014) showing returns of an equal-weighted commodity futures portfolio decade by decade. Both before and after inflation, we see that the latest decade is the only one where futures returns are below spot returns. In fact, they are substantially below. Something different is going on now after financialization of the commodity markets.

Even more telling is the following table from the Levine et al. paper itself:

Inside the red box are Sharpe ratios of the last two 20-year periods for portfolios with 90% of their assets in a 60/40 blend of stocks and bonds and 10% in commodities. (Allocating 10% of investment capital to commodity futures became widespread after the Gorton and Rouwenhorst paper in 2005.)
Portfolio Sharpe ratios take correlations, volatilities, and returns into account. The Sharpe ratios here are all about the same for the past 40 years. Thus, there has been no advantage in adding commodities to a balanced stock and bond portfolio. If you take front-running and commodity index fund expenses into account, an allocation to commodities would be even less desirable than a portfolio without them.

Aggregation Bias

Levine et al. say that their data extending back to 1877 implies that commodity futures add value to a diversified portfolio. Even if that is true, the nature of the commodity markets has changed significantly since the mid-2000s due to financialization. This means that data before financialization may not have as much relevancy. The Levine et al. optimal allocation to stocks, bonds, and commodities based on data from 1877 may not be the best one to use.

The three portfolio studies above, as well as the Levine et al. results over the past 40 years, may be a more accurate view of what to expect in the future. All four show that including commodities in a stock and bond portfolio is no longer beneficial.

Next Up

The reason I am discussing aggregation bias with commodities is two-fold. First, there are still many advisors and investors using commodities as a diversifier. They may want to reconsider that decision in light of the contrary evidence now.

The second reason has to do with factor-based investing. Factors have been growing in popularity and are expected to grow even more over the next few years. Yet they have many of the same issues as commodities with regard to aggregation bias, market impact, declining risk premia, front running, and unaccounted costs. This post has been an introduction to these issues. My next post will look at them in more detail in the light of factor-based investing.

[1] The CFTC does not always categorize hedgers and speculators correctly into commercial and non-commercial interests in their Commitments of Traders (COT) reports. The percentage of non-commercials mentioned here and elsewhere should be used with caution.

↧

Factor Zoo or Unicorn Ranch?

February 22, 2017, 5:16 am

≫ Next: Lessons Learned from Sports Investing

≪ Previous: Are Commodities Still a Good Portfolio Diversifier?

According to Morningstar, as of June 2016, the assets in smart beta exchange traded products totaled $490 billion. BlackRock forecasts smart beta using size, value, quality, momentum, and low-volatility will reach $1 trillion by 2020 and $2.4 trillion by 2025. This annual growth rate of 19% is double the growth rate of the entire ETF market. Are factors the cure-all for our investment needs? Or are they like “active management” that everyone wanted to have instead of passive index funds in the 1970s?

No one then wanted to be just average. This ironically gave many investors below average returns as they used the same information to compete against one another. Superior performance was usually due more to luck than to skill. But Bill McNabb, CEO of Vanguard, points out that passive index funds have been in the top quartile of long-term performance.

Factor-based investors and advisors now think they have an advantage. They base this belief on the results of theoretical asset pricing models, many of which have failed empirically.

Asset pricing models look at long-term long/short returns without taking into account the price impact of trading. Factors that looked good on paper may be lacking in robustness, pervasiveness, persistence, or intuitiveness.

Does Size Matter?

The small cap size premium was the first identified factor. Banz wrote about it in 1981. His results were influenced by extreme outliers from the 1930s.

Looking at more recent history, the oldest small cap index is the Russell 2000. It started in January 1979. Here is the Russell 2000 annual return and volatility over the life of the index compared to the S&P 500 index.

Russell 2000 underperformed the S&P 500 by 1.3% annually and had a substantially higher standard deviation. The Russell 2000 thus underperformed on both a risk-adjusted and non-risk adjusted basis.{1]

Here is a chart comparing the Sharpe ratios of all small and large cap stocks over a longer period of time. Small cap stocks usually failed to show significantly higher risk-adjusted profits than large cap stocks.

In the table below long-only small caps slightly outperformed large caps globally since 1982. But small caps have underperformed large caps in the U.S. since 1926. Where is the outperformance that Banz talked about?

According to Shumway and Warther (1998) in “The Delisting Bias in CRSP's Nasdaq Data and its Implications for the Size Effect”, small caps originally showed a premium because they had an upward bias due to inaccurate returns on delisted stocks. When this bias was removed, the small cap anomaly disappeared.

In “Transaction Costs and the Small Firm Effect,” Stoll and Whitney (1983) showed that transaction costs offset a significant portion of the small cap size premium.

Some researchers say a small cap premium still exists if you combine size with other factors. In other words, size can be important depending on what you can do with it. We will look at using value.

Front Running

Some attribute the poor performance of the Russell 2000 index to the actions of front runners. Index replicators follow formulas for trading. They have little control over what and when to trade. Their trades are also known by the public ahead of time.

I pointed out in my last post that front runners cost S&P GSCI index investors 3.6% in annual return. Front running can happen with any index or factor-based strategy having known portfolio rebalancing dates.

Front runners can initiate trades ahead of index replicators or smart beta fund managers. They then take profits after the replicators and fund managers finish their trading. Front runners thereby capture part of the factor or index return at the expense of index and fund investors.

If I were still managing hedge funds, I might front run rules-based strategies like value or momentum. These strategies often hold less liquid, more volatile stocks that offer the highest front running profits. Momentum would be a particularly attractive target. Its high portfolio turnover means more opportunities for profit.

Value - The Price is Right?

We all like bargains. Advisors and fund sponsors play off that desire by promoting the idea of a value premium. This past month I read two investment blogs saying cheap value stocks have outperformed the market by 4% per year. This, however, may be a case of theoretical results differing from actual ones.

A few months back, I referenced a study by Loughran and Hough that is worth mentioning again. These authors looked at the performance of all U.S. equity funds from 1962 through 2001. They used the prior 36 months to sort funds by style (top versus bottom quartile) and size (top versus bottom half).

Equal Weighted Mutual Fund Returns 1965 to 2002

Growth
Value
Difference
t-stat
Large Cap
11.30
11.41
0.11
-.05
Small Cap
14.52
14.10
-0.42
-.16
Source: Loughran and Hough (2006), “Do Investors Capture the Value Premium?”

From 1965 through 2001, the average large cap growth fund returned 11.30% per year, while the average large cap value fund returned 11.41%. This large cap outperformance of 0.11% of value over growth was insignificant.

With small caps, the authors were very surprised at the results. Small cap value funds earned 14.10%, while small cap growth funds returned 14.52%. Small cap value underperformed small cap growth by 0.42% per year.

Israel and Moskowitz (2012) presented evidence that the value premium is insignificant among the two largest quintiles of stocks and is concentrated among small cap stocks. So, why did small cap value funds underperform small cap growth funds?

Loughran and Hough said wide bid-ask spreads and the price impact of trading worked against the capture of a value premium in small-cap stocks. For value investing in general, they concluded, “We propose that the value premium is simply beyond reach…investors should harbor no illusion that pursuit of a value style will generate superior long-run performance.”

Some who want to believe in the superiority of value or small cap investing point to the performance of the Dimensional Fund Advisors (DFA) funds. Their U.S. Small Cap Portfolio (DFSTX) that began in March 1992 wasthe first factor-based small-cap fund. DFA's U.S. Large Cap Value Portfolio (DFLVX) and U.S. Small Cap Value Portfolio (DFSVX) funds began in February and March of 1993. All these funds have a positive alpha with respect ot he market. But none of the alphas are statistically significant. To the extent that the DFA fundshave done reasonably well may not be due only to their factor tilts.

DFA serves as a market maker in the stocks they hold. This means they can be patient when adjusting portfolio positions. This reduces their costs of trading in exchange for some additional tracking error. Using a buy-sell rangealso reduces turnover and trading costs. Holding a very large number of securities reduces the price impact of DFA's trading.

DFA has also benefited fromnot being tied to an index andthereby subject to front running costs. DFA has been aggressive in lending securities, as well. Additionally, DFA has avoided IPOs and stocks with high borrowing costs.

Stocks with high borrowing costs usually have a large short interest. This means there is a limited supply of stock available for borrowing. Studies here, here, and here show that heavily shorted stocks have significant negative abnormal returns.

Source: Boehmer et al. (2009), “The Good News in Short Interest”

Risk Factors

People may not remember that factors were once called “risk factors.” Value funds are known for their tracking error that can persist for 10 or more years. Value trap induced tracking error is a form of risk. It can cause investors and money managers to liquidate their positions at inopportune times.

Another risk is scalability. It might not be possible for popular strategies like value to always maintain an advantage over the market. This is particularly true of value stocks that are often out-of-favor and ignored. That can make them less liquid and more expensive to trade.

In “A Taxonomy of Anomalies Costs and their Trading Costs” Novy-Marx and Velikov (2015) looked at how capital levels can affect factor trading profits. Their calculations showed that excess profits disappear once the amount in value strategies exceeds $20.7 to $50.6 billion.

The Novy-Marx and Velikov capital levels are based on a turnover reducing approach. It buys value stocks ranked in the top 10th or 30th percentile. But it does not liquidate them until stocks drop out of the top 50th percentile. DFA, MSCI and others use a similar turnover reducing approach.

Here is a chart showing the amount of capital invested now in dedicated U.S. large and mid-cap value funds. It does not include managed accounts, hedge funds, and many of the other 400+ funds having the word “value” in their names.

U.S. Large Cap Value Index Funds	Assets
iShares Russell 1000 Value (IWD)	$35.2 b
Vanguard Value (VTV)	$27.6 b
DFA US Large Cap Value I (DFLVX)	$19.7 b
iShares S&P 500 Value (IVE)	$13.1 b
iShares Russell Mid Cap Value (IWS)	$9.4 b
Vanguard Mid Cap Value (VOE)	$6.6 b
TIAA-CREF Large Cap Value Index (TRLCX)	$6.3 b
DFA US Large Cap Value III (DFUVX)	$3.4 b
Schwab US Large Cap Value (SCHV)	$2.9 b
Total Value Assets	$124.3 b

The $124.3 billion in value funds exceeds the upper bounds where Novy-Marx and Velikov say value profits would disappear.

Momentum – the Premier Anomaly

Momentum is the strongest market anomaly based on academic research. Momentum has been studied now for more than 25 years. It meets all the tests of robustness, pervasiveness, persistence, and intuitiveness. It is with investability that momentum falls short.

Momentum performs best in focused, concentrated portfolios. Momentum is a high turnover strategy. Momentum stocks are often volatile with wide bid-ask spreads. Trading billions of dollars in a modest number of volatile stocks is bound to impact trade execution. It would be like trying to force a dozen people through a small door opening.

Academics have long been concerned about the price impact of momentum trading. The first to study this were Lesmond, Schill and Zhou (2002) in “The Illusive Nature of Momentum Profits.” They found that momentum creates an illusion of profit opportunity when none really exists. Two years later, Korajcyzk and Sadka (2004) determined that profit opportunities could vanish once the amount invested in momentum-based strategies reaches $5 billion.

Counter to these findings, Frazinni, Israel and Moskowitz (2012) from AQR, based on 12 years of proprietary data, argued that the potential scale of momentum is more than an order of magnitude greater than previous studies suggested. They said this capacity could increase even further by using optimized trading methods.

More recently, Ratcliffe, Miranda and Ang (2016) from BlackRock also suggested that a greater amount of capital could be traded using momentum. But they also made this disclaimer, “The exercise we conduct in this paper is hypothetical and involves several unrealistic assumptions.”

In contrast to these two studies, Fisher, Shah and Titman (2015), using observed bid-ask spreads, got results much closer to those of Lesmond et al. and Korajcyzk & Sadka than Frazinni et al.

Novy-Marx and Velikov (2015) also determined the capacity for stock momentum before profits would vanish.

This is close to the $5 billion amount where Korajcyzk and Sadka said momentum profits would disappear. Novy-Marx and Velikov used an optimization algorithm to keep them in trades longer, as discussed by Frazzini et al.

Here is a table of the amounts invested in U.S. momentum exchange traded ptoducts:

This is a conservative listing. It does not include mutual funds, managed accounts, or hedge funds. Even so, it exceeds the level of assets where both Novy-Marx and Velikov and Korajcyzk and Sadka say momentum profits would no longer exist.

Here is a table from the most recent study of factor capacity. It is by Beck, Hsu, Kalesnik and Kostka (2016) in “Will Your Factor Deliver? An Examination of Factor Robustness and Implementation Costs.” They used a different method than Novy-Marx and Velikov to compute factor capacity.

With $10 billion invested in large cap momentum, the value added by momentum goes from +2.7% per year before transaction costs to -3.4% after transaction costs. This is with monthly portfolio rebalancing. If you rebalance quarterly instead of monthly, your additional annual return goes from +2.0% before trading costs to -1.6% afterwards. The expected future growth in factor-based investing should make this worse.

This situation is much like the one in my last post. Those offering commodity products to the public said passive commodities are still a worthwhile diversification. But a larger number of independent researchers, with no products to promote, said the opposite. Who shall we believe?

Let us look at the performance of the oldest publicly available momentum funds. First is the PowerShares DWA Momentum ETF (PDP) managed by Dorsey Wright. It began on March 1, 2007. The second is the AQR Large Cap Momentum (AMOMX) mutual fund. It began on July 9, 2009.

From its start through January 2017, PDP had an annual return of 6.44%, while its Russell 3000 Growth benchmark returned 8.67%. This is an average annual return shortfall of 2.23%. PDP has had a focused portfolio of 100 momentum stocks. AMOMX had an annual return of 14.55% since its inception, while its Russell 1000 Growth benchmark returned 16.11%. This is an average annual shortfall of 1.56%. These are short periods of time to evaluate performance. But it does suggest some caution.

Besides managing seven momentum mutual funds, AQR uses momentum with their multi-style funds and large hedge fund. Even though the Frazinni et al. paper said stock momentum could handle considerably more capital, AQR now spreads out their momentum holdings to 496 stocks. This is half the fund’s available universe of 1000 stocks.

Quality

We can find intuitive reasons why size, value, and momentum might provide a premium based on risk or behavioral factors. This becomes more challenging with quality. Why should quality stocks be mispriced in the marketplace? There is no reason to believe that higher quality stocks are riskier than lower quality ones. It is also hard to find behavioral factors that would explain why one would neglect high quality stocks causing them to command a behavioral premium. It is not surprising then that there are few signs of a premium or premium persistence across multiple definitions of quality.

Cakici (2015) found only marginal evidence that gross profitability (a subset of quality) exists globally. Hsu and Kalesnik (2014) reported in “Finding Smart Beta in the Factor Zoo” that two measures of quality (gross profitability and ROE) in international stocks from 1987 through 2013 showed no significant improvement in Sharpe ratio over lower quality stocks. They also found no evidence of a significant advantage in U.S. stocks using four measures of quality from 1967 through 2013:

Multi Factor Portfolios

West, Kalesnik and Clements (2016) in “How Not to Get Fired in Smart Beta Investing” included quality in a multi-factor environment.

They determined that quality, value, and momentum are a non-robust combination. Why is this important?

More multi-factor ETFs were created in the last two years than any other category of ETF. In a January 2016 survey by Greenwich Associates, 57% of institutional investors said they used multi-factor funds in some way now. 48% said they plan to increase their use soon.

Multi-factor portfolios have less volatility and reduced tracking error compared to single factor portfolios. In “A Smoother Path to Outperformance with Multi-Factor Smart Beta Investing,” Brightman, Kalesnik, Li and Shim (2017) show that annual volatility drops from 16.4% for an average factor to 15.2-15.6% for a multi-factor portfolio. This reduction is desirable. But those familiar with portfolio theory know that factor portfolio returns are a weighted average of factor returns. If factor returns are disappointing due to lack of scalability (value and momentum), data accuracy and persistence (size), or robustness (quality), multi-factor returns will also be disappointing. In addition, multi-factor portfolios can face greater uncertainty due to selection bias and increased data mining.

On the positive side, a multi-factor approach can cut benchmark tracking error in half. But would it really matter if 10 years of factor-based underperformance were reduced to 5 years? Small cap value once underperformed the market for 42 consecutive months. If that had been 21 months, would it have made much difference? Most investors would have been gone long before then.

Low Volatility

In a Brown Brothers Harriman survey of 175 financial advisors and institutional investors, low volatility was the most popular smart-beta choice. 44% of respondents chose low volatility over other factors. There is no risk-based reason why low-volatility/low beta stocks should outperform their counterparts. But a case can be made that leverage constraints can cause high volatility stocks to be bid up so they are overpriced relative to low-volatility stocks.

However, low volatility is the most problematic factor. The first cautionary sign is a chart of pre-1967 performance in the appendix of Novy-Marx’s (2016) paper “Understanding Defensive Equity.” Volatility and beta are estimated using daily data from the prior year when available. Otherwise, Novy-Marx uses 5 years of monthly data.

There is little difference between the lowest and highest volatility quintiles. With respect to beta, low beta is the worst performer, while high beta turns in the second-best performance. These results contradict those found by Novy-Marx and others since 1968.

Novy-Marx also pointed out that the vast majority of low volatility profits since 1968 came from the short side. He showed that most of the benefits from low volatility investing could be achieved simply by eliminating small growth stocks from one’s portfolio.

In “The Limits to Arbitrage and the Low-Volatility Anomaly,” Li, Sullivan and Garcia-Feijoo (2014) found that the excess return associated with low volatility was present only in the first month after portfolio formation. Additionally, excess return has been weak since 1990. They also found that the low volatility premium was largely offset by high transaction costs. It was largely eliminated if you omitted stocks priced under $5 per share. It also was not present in equal weight portfolios.

Garcia-Feijoo, Kochard, Sullivan and Wang (2015) in “Low-Volatility Cycles: The Influence of Valuation and Momentum on Low-Volatility Portfolios,” showed that the excess return from low-volatility is reliably positive only when low-volatility stocks are much cheaper than high volatility stocks as shown by a high book-to-price (B/P) ratio.

Using U.S. stock data from 1929 through 2010, van Vliet (2012) found low-volatility has had time-varying exposure to the value factor. When low-volatility stocks had value exposure, they returned an average of 9.5% annually versus the market’s 7.5%. But when low-volatility stocks had growth exposure, they returned 10.8% annually versus the market’s 12.2%.

Getting back to the idea of short interest, Jordan and Riley (2016) show in “The Long and Short of the Vol Anomaly,” that short interest dominates the low-volatility effect from July 1991 through December 2012.

High volatility stocks with low short interest had extraordinarily positive returns. High volatility stocks with high short interest had extraordinarily poor returns. Low volatility stocks had a similar, but less dramatic, disparity in performance based on short interest. Short interest has had a great impact on low-volatility performance.

Summarized here are the issues associated with the low-volatility premium:

•    Weak since 1990
•    Absent in higher priced stocks
•    Exists mostly on the short side
•    Largely offset by transaction costs
•    Reliably positive only when cheap
•    Not present in equal weight portfolios
•    Present only in the first month after formation

Less Downside Risk

With all these negatives, one might wonder why low-volatility has been the fastest growing factor. This may have to do with investors thinking low-volatility has less risk exposure than the market. It is not surprising that investors are more risk-averse now. They have experienced two bear markets over the past 20 years where stocks lost half their value.

How much risk reduction is there really from low-volatility investing? To find out, I accessed the online data provided by van Vliet and De Koning. They used the 1000 largest NYSE, AMEX, and NASDAQ stocks over $1 per share in the CRSP database. Stocks were equal weighted and sorted into deciles based on their volatility over the past 36 months. These portfolios were rebalanced quarterly.

I accessed the data starting in January 1934 to avoid the extreme returns of the late 1920s and early 1930s. I used the top two low-volatility deciles, representing 200 stocks, which is a typical fund-size portfolio. I compared the performance of the low-volatility portfolio to the S&P 500 and to a robust version of trend following absolute momentum that I use in my proprietary dual momentum models. Absolute momentum holds the S&P 500 when the model is in stocks and intermediate U.S. Government bonds when the model is out of stocks. Data is from Ibbotson Associates.

Jan 1934 – Dec 2014	S&P 500	Low-Volatility	Absolute Momentum
CAGR	11.1%	12.3%	13.2%
Standard Deviation	15.8%	12.3%	11.3%
Sharpe Ratio	0.53	0.73	0.85
Worst Drawdown	-50.9%	-40.1%	-31.5%

Worst U. S. Bear Markets 1934- 2014

S&P 500
Low-Volatility
Absolute Momentum
Jul 2007 – Feb 2009
-50.9%
-38.3%
+5.0%
Apr 2000 – Sep 2002
-43.8%
+24.2%
+17.4%
Jan 1973 – Sep 1974
-41.8%
-37.5%
+2.0%
Nov 1968 – Jun 1970
-29.3%
-22.9%

-2.9%
Mar 1937 – Mar 1938
-50.5%
-40.1%
-9.1%

The low-volatility portfolio outperformed the S&P 500. But absolute momentum was more effective at both reducing drawdown and enhancing return.

For those who want more evidence on the efficacy of trend following, here are the results from Greyserman and Kaminski’s test of 12-month absolute momentum applied to stocks, bonds, commodities, and currencies back to the year 1223! Assets were held in cash when out of the markets. Trend following absolute momentum was much more effective than buy-and-hold. The sizes of the five largest drawdowns were also reduced by an average of one-third.

Source: Greyserman and Kaminski (2014), Trend Following with Managed Futures

The viability of trend-following momentum back to the 13th century is strong evidence that it is not an artifact of data mining.

Conclusion

Each factor that I looked at failed to hold up under one or more of these tests: robustness, persistence, pervasiveness, intuitiveness, and investabilty. But short interest and trend appear to be effective ways to enhance portfolios.

The usual factors may look good in theory and on paper. But the jury is out on whether or not they can provide superior risk-adjusted real world returns after costs. Those who are prudent and truly interested in evidence-based investing will remain cautious. Others will continue to accept what they have been told by product sponsors and a small number of academic theorists.

[1] For more on the the Russell 2000 index and its issues, see Alpha Architect's "A Better Way to Buy the Russell 2000".

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Lessons Learned from Sports Investing

April 12, 2017, 6:16 am

≫ Next: Real Time Factor Performance

≪ Previous: Factor Zoo or Unicorn Ranch?

Wee Willie Keeler was one of the greatest contact hitters in baseball. One year, 30 of Keeler’s 33 home runs were inside the park. Keeler’s motto was, “Keep your eye clear, and hit ‘em where they ain’t.”

I have always tried to do that by focusing on underexploited investment opportunities. In the 1970s that meant stock options. In the 1980s I had success with managed futures.

Also in the 1980s I had a family member who bet on football games. He knew I invested using data-driven quantitative methods, so he asked me to take a look at betting NFL home underdogs. I was reluctant at first but then obliged him. I was surprised to discover profit opportunities there.

I became intrigued with the possibility of exploiting inefficiencies in that market. There were no computer-based sports databases back then and almost no published sports research. So I hired a few UC Berkeley students to go through the data and help me test betting strategies.

After we had a stable of successful angles, I put one of these students on a bus to Reno each weekend. Encouraged by our early results, I expanded this research to include all sports, both pro and college.

I focused on areas where the linemakers were not paying enough attention, such as game time weather conditions or mean reversion in team stats. My wife never understood why I was always so interested in the wind direction at Wrigley Field.

We even came up with a player stat based Monte Carlo simulator that predicted the outcome of every baseball game. It gave us an edge early in the season before others figured out the impact of all the off-season player trades.

One of my research assistants continued to analyze sports after graduation. He became Vice President of Basketball Operations for an NBA championship team. He is now VP of Basketball Strategy and Data Analysis with another NBA team.

Our biggest edge came from betting against public biases. For example, teams that showed poor performance in their last game were often under bet in their next game. As with stock market investing, mean reversion and public myopia were rampant in sports wagering. (My best indicator of positive future results has always been when investors overreact to short-term losses or underperformance and close out their accounts.)

Issues with Doing Well

As we continued to do well, some bookmakers would no longer take our action. One let us bet early so he could use that information to move their lines. Another became very friendly and would bring us other bookmakers’ lines as soon as they were available. This way he could know most of our plays and bet right along with us.

Afternoons we would hang large marking boards on the walls of our investment office and write down the betting lines from all our outs. Fortunately, we had very few office visitors!

I had a 12-foot BUD (Big Ugly Dish to get all the satellite feeds) installed at my house and would watch as many games as I could. That was the problem. Sports wagering was causing me to neglect my family, so I set it aside. (I won’t say I gave it up, since it would be a great out-of-sample test to sometime see how those angles have held up since then.)

Looking back on my sports activities, I realize now that I learned valuable lessons that helped make me a better researcher and investor. Here are some of them:

Always Have an Edge

When I went to Nevada with friends, I would never play casino games. When they asked why, and I said, “I don’t gamble,” they would laugh. They knew I was betting tens of thousands of dollars every week on sporting events. I always wanted a positive expectation of profit before assuming any risk. To me, this is what distinguished what I was doing from gambling.

Most of those who invest actively have little or no edge. You cannot have an advantage doing what everyone is doing. You would generally be better off investing in low-cost passive index funds. As I indicated in my last blog post, factor-based investing may soon pose the same problem. My need for a positive expectation led me instead to the little exploited niche of dual momentum investing.

Do Your Homework

Betting lines, like financial markets, are mostly efficient. The only way to be confident you have an edge is through thorough research using plenty of data. Doing your homework gives you confidence. It helps you stay with your approach despite short-term fluctuations in the value of your investments.

For investors, this can mean not doing what everyone else is doing. Herding is a powerful behavioral instinct, but it can lead to mediocre or worse investment returns. You need to have a healthy dose of skepticism about all strategies that differ from the market portfolio. This also means looking beyond academic studies. You need to be aware of how strategies actually perform real time in light of scalability and liquidity issues. And you need to consider how they will perform in the future as they attract more capital. [1]

Keep Things Simple

Selection bias, over optimization, and model overfitting are serious problems in both sports and non-sports research. If you keep tweaking a strategy, it isn’t difficult to find betting angles that look like they have over 60% winners. But these almost never hold up in real time.

With sports wagering you need 52.4% winners to break even after costs. Sports betting legend Lem Banker became wealthy with an overall winning percentage of around 57%.

Sports research taught me the importance of having a simple strategy with intuitive logic behind it. You also need plenty of backtest data across different markets. This is what led me to momentum investing. It is simple, logical, and supported by over 200 years of backtest validation across nearly all markets.

Have Realistic Expectations

If you win 57% of your sports bets, you are still going to have some serious losing streaks. You just have to accept this. Warren Buffett is often quoted as saying the # 1 rule of investing is to not lose money, and the # 2 rule is to never forget rule #1. That is nonsense. Buffett’s Berkshire Hathaway was down more than 50% twice during the past 15 years. Yet Buffett has still done well. Confidence in your approach and emotional discipline are really what you need once you have a proven edge.

Expecting to consistently win at sports much more than 60% of the time is unrealistic. Expecting to beat the markets most of the time on a short-term basis is also unrealistic. Here is the percentage of time that Global Equities Momentum (GEM) featured in my book outperformed the S&P 500 index over various periods since 1971:

Time horizon	% of time GEM outperformed the S&P 500
3 months	52%
1 year	55%
3 years	71%
5 years	85%
10 years	99%

Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Please see our Disclaimer page for more information.

Over one year or less, GEM did not do much better than a coin flip. But over 5 or more years, those results change considerably. Patience is important whether you are a traditional investor or have a 57%-win rate from sports. Warren Buffett did have the right idea when he said the stock market is a mechanism for transferring wealth from the impatient to the patient.

Leave Your Opinions at the Door

You need to forget your likes or dislikes and go where the data takes you to be an effective sports bettor. The same is true for investing. I have seen many investors disregard or override their strategies when these conflicted with their hopes or cherished beliefs. Some close their accounts or decline to open new accounts because of their behavioral biases or fears. To be a winner over the long run, you need to be a good loser over the short run. You can do this if you have a proven edge with a simple approach, have done your homework, and have realistic expectations. Go Patriots!

[1] For more on this, see my blog post "Factor Zoo or Unicorn Ranch" and Research Affiliates'"The Incredible Shrinking Factor Return".

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Real Time Factor Performance

June 10, 2017, 10:32 pm

≫ Next: Trend Following Research

≪ Previous: Lessons Learned from Sports Investing

According to S&P DJ Indices, 92% of all actively managed stock funds failed to beat their benchmarks over the past 15 years. This should come as no surprise. Similar results were published more than 20 years ago. This information has caused a move away from active stock selection and toward index funds or systematic approaches.

Money managers have recently moved more in the direction of factor-based and so-called smart beta investing. But as I pointed out in my February blog post, “Factor Zoo or Unicorn Ranch?”, there are some serious issues with this type of investing. Not the least of which is the shortfall between actual and theoretical returns.

Theoretical results are in academic papers and all over the internet. Very little information is available on the real-time performance of factor-based investments.

Lack of Real Time Performance Studies

The Loughran and Hough study in 2006 was a rare look at real-time factor performance. In it, the authors showed that there was no significant difference in performance between U.S. value and growth mutual funds from 1965 through 2001. The authors concluded by saying the idea that value generates superior long-run performance is an “illusion”.

This was the only study I could find that examined actual rather than theoretical results of a popular investment factor. But now there is another study. Last month Arnott, Kalesnik, and Wu (AKW) published an article called, “The Incredible Shrinking Factor Return.”

AKW examined actual versus theoretical performance of four factors well-known to investors using 5323 mutual funds from January 1991 through December 2016. These factors are market, value, size, and momentum

Two Step Approach

To determine their results, AKW did a two stage (Fama-MacBeth) regression. In stage 1, they regressed mutual fund returns against the excess return of each factor to figure each fund’s average factor loadings. In stage 2, they regressed fund returns against the average factor loadings to get the return of each fund per unit of factor exposure. These were then compared to the fund factor returns.

This approach is a good one since it incorporates factor covariances to determine factor premia. Comparing actual to theoretical performance can reveal data mining, selection, and survivorship biases. It can also identify the effects of management fees, bid-ask spreads, and transaction costs.

In many academic papers, more than half the profits come from shorting stocks. But shorting may be expensive and sometimes impossible to do. Looking here at long-only mutual fund performance removes those unrealistic profits.

Performance Shortfalls

Here are AKW’s regression results using 25 years of fund data from January 1991 through December 2016:

We see a 50% shortfall in the performance of the market factor. This is not surprising. For many years, other research has shown this effect. High beta tends to underperform low beta on a risk adjusted basis going forward in time.

In the AKW regression, the size factor shows a small but insignificant improvement in actual versus theoretical returns. This may be data noise.

Value is the most commonly used factor. AKW’s regression shows that value fund managers captured only 60% of the value premium since 1991. This compliments the recent findings of Kok, Ribardo & Sloan (2017). They claim that outside the initial evaluation period of 1963 to 1981, the evidence of a value premium is weak to non-existent. Value is suspect now, both on a theoretical and actual basis.

The largest shortfall AKW discovered is with momentum. The realized momentum return of live portfolios was close to zero compared to a theoretical return of around 6% per year. AKW say transaction costs play a major role as the source of slippage between theoretical and realized factor returns. In their words, “…higher turnover strategies, such as momentum, have trading costs that may be large enough to wipe out the premium completely if enough money is following the strategy.”

AKW concludes their study by asking if 10,000 quants all pursue the same factor tilts, how likely is it that these factors will add value? I asked the same question several months ago in my post.

Skepticism and Pushback

Skepticism toward new information may be a good thing. More research and analysis can help advance what we know about the world.

Corey Hoffstein offers a critical response to the AKW study in an article he calls “A Simulation Based Rebuttal to Research Affiliates.” Corey points out one should not overlook style drift as a significant source of error. Return estimates can be inaccurate if managers switch investing styles. In support of this, Corey shows 3-year rolling betas versus full period betas for the Vanguard Wellington Fund (VWELX). His data is from January 1994 through July 2016.

Corey’s logic is like saying one should be suspicious of the 10% average return of the S&P 500 index over the past 50 years because yearly returns have varied from -37% to 38%. One would never expect to earn 10% every year going forward.

In addition to the full data set, AKW looks at an expanding window of returns that incorporates all the data available up to that point. An expanding window regression converges to the full sample factor betas toward the end of the sample period. When AKW compares expanding window regressions to full period ones, they get comparable results.

Corey’s second argument is that you can attribute a portion of the AKW identified shortfall to estimation error. Factor loading estimates are noisy. Estimation error in the independent variables creates a pull toward zero in the beta coefficients. This causes a downward biasing of factor premia estimates in the second stage of AKW’s regression.

Corey offers no direct evidence of how much bias there is in the AKW regression. Instead, Corey conducts a 1000 hypothetical fund simulation using normally distributed betas.

There are some good reasons why simulations are rarely used in financial markets research. Simulations are dependent on distributional assumptions that are unrealistic with financial markets. Market returns are not independent, and their underlying distributions are non-stationary.

In his simulation, Corey assumes that returns are normally distributed, which is not the case for mutual fund returns. Nor does Corey show that estimation errors have the same distribution scale as the betas themselves.

Academic researchers prefer to use as much real data as they can rather than simulated data. The AKW regression uses 25 years of actual mutual fund data, which should be enough to minimize the influence of tracking error on AKW's results.

Corey uses only one fund, VWEIX, with his simulation to estimate how much downward bias there might be in the AKW regression. He looks at the differences in standard deviation between full period and rolling estimates of VWELX’s beta coefficients.

From this one fund, Corey concludes there may be significant downward bias in the AKW regression estimates. He does not explain why there are different degrees of slippage for the different factors. In the end, Corey says, "our results do not fully refute AKW’s evidence".

AKW also mentions this downward bias in betas due to estimation error in the independent variables. They conduct six different robustness tests that reinforce their results and help mitigate that error. Those results are consistent with AKW’s core findings.

We Are All Biased

I applaud Corey’s skepticism with regard to unexpected research findings. I also applaud him when he says, “…published research in finance is often like a back test. Rarely do you see any that does not support the firm’s products or existing views.”

We see that with many advisors and fund managers, as well as throughout the blogsphere. But we should keep in mind that this logic works both ways. Those who adhere to alternative approaches are often the ones who challenge new ideas. We should apply some healthy skepticism to both sides of such controversies.

↧

Trend Following Research

July 14, 2017, 4:55 am

≫ Next: Book Review: Standard Deviations, Flawed Assumptions, Tortured Data and Other Ways to Lie with Statistics

≪ Previous: Real Time Factor Performance

There have been hundreds of research papers on relative strength momentum since the seminal work by Jegadeesh and Titman in 1993. [1] Relative momentum has been shown to work in and out-of-sample within and across most asset classes. Theoretical results have been consistent, persistent, and robust.

Research on trend following absolute momentum got a much later start. The first paper on “Time Series Momentum” was by Moskowitz, Ooi, and Pedersen (2012). [2]

This was followed by my "Absolute Momentum: A Simple Rules-Based Strategy and Universal Trend Following Overlay" in 2013.

Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained.

Since then, there have been other good absolute momentum research papers. But absolute momentum and trend following in general have still not gotten the attention they deserve. Major fund sponsors offer single or multi-factor products using relative momentum. But not a single one incorporates absolute momentum as a trend filter.

Absolute momentum can enhance expected returns just like relative momentum. But, unlike relative momentum, absolute momentum can also reduce expected downside risk exposure. It performs best in extreme market environments, making it an excellent portfolio diversifier.

Moving Averages

Let us look at other trend following research over the past few years. In 2014, Lemperiere et al. applied exponential moving averages to futures since 1960. They examined spot commodities and stock indices since 1800. [3] Their “Two Centuries of Trend Following” showed a t-statistic of 5 on excess returns since 1960 and a t-statistic of 10 on excess returns since 1800. These results were after accounting for the upward drift of the markets. The effect was stable across time and asset classes. There was also no degradation of long-term trend strength in recent years.

In "Timing the Market with a Combination of Moving Averages," Glabadanidis (2016) presented ample evidence of the timing ability of a combination of simple moving averages applied to U.S. stocks.

A comprehensive treatment of moving averages is in the book Market Timing with Moving Averages: The Anatomy and Performance of Trading Rules by Valeriy Zakamulin. This book will be published in September. Zakamulin has already written academic papers on moving average methods.

In the book, Zakamulin analyzed eight different types of moving averages along with absolute momentum. He applied these to stocks, stock indices, bonds, currencies, and commodities since 1857. He showed that these strategies can protect portfolios from losses when needed the most. Zakamulin's conclusion was that trend following represents a prudent investment approach for medium and long-term investors.

What was especially interesting to me was Zakamulin’s 159-year test on the S&P Composite Index. He looked at the frequency of positive results using a 10-year rolling performance window. Absolute momentum came in first and second place among the strategies tested. It also held 7 out of the top 10 highest positive rankings.

Absolute Momentum

There have been at least a half-dozen noteworthy studies during the past few years that focused on absolute momentum.

In Trend Following with Managed Futures, Greyserman and Kaminski (2014) applied absolute momentum to stock indices, bonds, commodities, and currencies all the way back to 1223! They held assets long or short depending on the trend of the last 12 months. The authors found that trend following was much more effective than buy-and-hold. Sizes of the five largest drawdowns were also reduced by an average of one-third.

In “The Trend is Your Friend: Time-Series Momentum Strategies Across Equity and Commodity Markets,” Georgopoulou and Wang (2016) found that absolute momentum was significant, consistent, and robust across conventional asset classes from 1969 to 2015.

In “Trend Following: Equity and Bond Crisis Alpha,” Hamill, Rattray & Van Hemert (2016) applied absolute momentum to global diversified markets from 1960 through 2015. Absolute momentum performed consistently and was particularly strong duing the worst equity and bond environments.

In “The Enduring Effect of Time Series Momentum on Stock Returns Over Nearly 100 Years,” D’Souza et al. (2016) found significant profits from absolute momentum applied to individual U.S. stocks from 1927 to 2014 and to international stocks from 1975. Unlike relative momentum, absolute momentum did well in both up and down markets. Absolute momentum fully subsumed relative momentum and was not subsumed by any other factor. The combination of relative and absolute momentum (dual momentum) earned a striking 1.88% per month (t-statistic 5.6).

In “Two Centuries of Multi-Asset Momentum (Equities, Bonds, Currencies, Commodities, Sectors and Stocks),” Geczy and Samonov (2017) applied relative momentum to country indices, bonds, currencies, commodities, sectors, and U.S. stocks over the past 215 years. But they also showed that absolute momentum (which they called “trend”) had highly significant positive results in every asset class.

In “Time-Series and Cross-Sectional Momentum Strategies under Alternative Implementation Strategies,” Bird, Gao, and Yeung (2017) found that both relative and absolute momentum generated positive returns in 24 major stock markets from 1990 through 2012. But absolute momentum was clearly superior. With appropriate cutoffs, absolute momentum outperformed in all 24 markets. The authors concluded that momentum is best implemented using absolute momentum.

A recent study of absolute momentum by Hurst, Ooi, and Pedersen (2017) is an extension of their earlier paper, “A Century of Evidence on Trend-Following Investing.” In it, the authors studied the performance of trend-following across global markets (commodities, bond indices, equity indices, currency pairs) since 1880. They found in each decade since 1880, absolute momentum delivered positive average returns. This was accomplished with low correlation to traditional asset classes and after adjustments for fees and trading costs.

Absolute momentum performed well across different macro environments and in 8 out of 10 of the largest crisis periods. It performed best during extreme up and down markets in U.S. stocks.

Implications

The non-acceptance of absolute momentum as a trend filter by most fund sponsors in the face of strong evidence of its effectiveness has three likely explanations. The first is that research information disperses very slowly through the investment community. The second is that investors prefer to follow the crowd, even if that means losing more in down markets. They may also be especially averse to experiencing trend following whipsaws. The third reason is the possible long-standing bias against trend following. This has been difficult to dislodge, despite strong evidence of its effectiveness.

Based on the above research results, trend may very well be the strongest factor. Yet it is also the most ignored factor. This is good news for those of us using it.

There are serious questions about the real-time efficacy of factor-based investing, especially for the future. This is because of over exploitation and capacity constraints with some factors, as explained here and here. It looks like trend followers have nothing there to worry about.

[1] Many academic papers refer to relative momentum as cross-sectional, even though some applications are cross-asset, not cross-sectional.
[2] Academic papers often refer to absolute momentum as time series momentum. But all momentum is based on time series. Geczy and Samonov (2017) repeatedly characterize all momentum as time series momentum in their latest paper.
[3] For a theoretical justification of trend following, see Zhu and Zhou's (2009), “Technical Analysis: An Asset Allocation Perspective on the Use of Moving Averages.”

↧

Book Review: Standard Deviations, Flawed Assumptions, Tortured Data and Other Ways to Lie with Statistics

August 12, 2017, 9:33 am

≫ Next: Factor Investing: Implementation Costs Do Matter

≪ Previous: Trend Following Research

Years ago, when asked to recommend some good investment books, I often suggested ones dealing with the psychological issues influencing investor behavior. These focused on investor fear and greed, showing “what fools these mortals be.” Here are examples: Devil Take the Hindmost: A History of Financial Speculation by Edward Chancellor, and Extraordinary Popular Delusions and the Madness of Crowds by Charles MacKay.

In recent years, there has been a wealth of similar material in the form of behavioral finance and behavioral economics. I now suggest that investors do an internet search on these topics. To better understand investing and investors, you should be familiar with concepts like herd mentality, recency bias, confirmation bias, overconfidence, overreaction, loss aversion, and the disposition effect.

An enjoyable introduction to this field is Richard Thaler’s Misbehaving: The Making of Behavioral Economics. Here is an extensive bibliography for those who want to do a more in-depth study.

Importance of Statistical Analysis

Now that quantitative investment approaches (factors, indexing, rules-based models) are becoming prominent, you need to also be able to properly evaluate quantitative methods. A lively book on the foundations of statistical analysis is The Seven Pillars of Statistical Wisdom by Stephen Stigler. An engaging and more nuanced view of the subject is Robert Abelson's Statistics As Principled Argument.

What I mostly recommend is Standard Deviations, Flawed Assumptions, Tortured Data and Other Ways to Lie with Statistics by economist Gary Smith. Everyone should benefit from reading this book.

Smith’s premise is that we yearn to make an uncertain world more certain and to predict the unpredictable. This makes us susceptible to statistical deceptions. The investment world is especially susceptible now that it is more model-based and data driven.

It is easy to lie with statistics but hard to tell the truth without them. Smith takes up the challenge of sorting good from bad using insightful stories and entertaining examples. Here are some salient topics with real-world cases that Smith covers:

•    Survivorship and self-selection biases
•    Overemphasis on short-term results
•    Underestimating the role of chance
•    Results distorted by self-interest
•    Correlation is not causation
•    Regression to the mean
•    Law of small numbers
•    Confounding factors
•    Misleading graphs
•    Gamblers fallacy

Critical Thinking

Smith is not afraid to point out mistakes by the economic establishment. He mentions errors by University of Chicago economist Steven Levitt of Freakonomics fame. Smith also discusses research made popular by two Harvard professors, Reinhart and Rogoff. The professors concluded that a nation’s economic growth is imperiled when its ratio of government debt to GDP exceeds 90%. Smith points out serious problems with their work due to inadvertent errors, selective omissions of data, and questionable research procedures.

Publish or perish can contribute to errors in academic research. Economic self- interest, as in medical and financial research, can also cause errors. Smith helps us see how important it is to look at research critically instead of blindly accepting what is presented.

Theory Ahead of Data

Throughout his book Smith focuses on the potential perils associated with deriving theories from data. He gives examples of the Texas sharpshooter fallacy (aka the Feynman trap). Here a man with a gun but no skill fires a large number of bullets at the side of a barn. He then paints a bullseye around the spot with the most bullet holes. Another version is where the sharpshooter fires lots of bullets at lots of targets. He then finds a target he hits and forgets the rest. Predicting what the data looks like after examining the data is easy but meaningless. Smith says:

Data clusters are everywhere, even in random data. Someone who looks for an explanation will inevitably find one, but a theory that fits a data cluster is not persuasive evidence. The found explanation needs to make sense, and it needs to be tested with uncontaminated data.

Financial market researchers often use data to help invent a theory or develop a trading method. Theory or method generated by ransacking data is a perilous undertaking. Tortured data will always confess something. Pillaged data without theory leads to bogus inferences.

Data grubbing can uncover patterns that are nothing more than coincidence. Smith points to the South Seas stock bubble as an example where investors saw a pattern - buy the stock at a certain price and sell it at a higher price. But they didn’t think about whether t it made any sense.

Smith addresses those who take a quantitative approach to investing. He says quants have “a naïve confidence that historical patterns are a reliable guide to the future, and a dependence on theoretical assumptions that are mathematically convenient but dangerously unrealistic.”

Common Sense

Smith’s solution is to first make sure that one’s approach makes sense. He agrees with the great mathematician Pierre-Simon LaPlace who said probabilities are nothing but common sense reduced to calculation.

Smith says we should be cautious of calculating without thinking. I remember a case at the Harvard Business School where we looked at numbers trying to figure out why Smucker’s new ketchup was not doing well as a mass market item. The reason turned out to be that no one wanted to buy ketchup in a jam jar. We need to look past the numbers to see if what we are doing makes sense.

I often see data-derived trading approaches fit to past data without considering whether the approaches conform to known market principles. If one does come up with models having sensible explanations, they should then be tested on new data not corrupted by data grubbing. Whenever you deviate from the market portfolio, you are saying you are right and the market is wrong. There should be some good reasons and plenty of supporting data for believing this is true. [1]

Self-Deception

I have seen some take the concept of dual momentum and make it more complicated with additional parameters. They may hold back half their data for model validation. They call this out-of-sample testing, but that is a questionable call.

Do you think you would hear about these models if their “out-of-sample” tests showed poor results? Would they discard their models and move on? Chances are they would search for other parameters that gave satisfactory results on both the original and hold out data.

Momentum is robust enough that a test on hold out data might look okay right from the beginning. But that is likely due to momentum’s overall strength and pervasiveness. It is questionable whether you really had something better than with a simpler approach. You may have just fit past data better, which is easy to do by adding more parameters.

Keep It Simple

There are some areas I wish Smith had addressed more. First is the importance of having simple models, ala Occam’s razor. Simple models dramatically reduce the possibility of spurious results from overfitting data.

Overfitting is a serious problem in financial research. With enough parameters, you can fit almost any data to get attractive past results. But these usually do not hold up going forward.

John von Neumann said that with four parameters he can fit an elephant, and with five he can make it wiggle its trunk.In the words of Edsger Dijkstra, "Simplicity is a great virtue, but it is requires hard work to achieve it and education to appreciate it. And...complexity sells better."

In Data We Trust

Smith briefly mentions the law of small numbers popularized bt Kahnemann and Tversky. But I wish Smith had gone more into the importance of abundant data and how it helps us avoid biased results.

According to de Moivre’s observation, accuracy is proportional to the square root of the number of observations. To have half the standard error, you need four times the data.

The stock market has had major regime pattern changes about every 15 years. A model that worked well during one regime pattern may fail during the next. We want a robust model that holds up across all regimes. To determine that, you need plenty of data. In the words of Sherlock Holmes, “Data! Data! Data! I can’t make bricks without clay!”

Theories Without Data

Smith talks a lot about the issue of data groping without theory. But he also mentions the opposite problem of theory without adequate data analysis. Smith cites as an example the limit to growth theories of Malthus, Forrester, and Meadows. Smith contends they did not make any attempt to see whether historical data supported or refuted their theories. Most economists now dismiss these theories.

But Smith may not have considered later information on this topic. Here and here is information that makes this subject more provocative. Smith says it is good to think critically. This is true even when approaching a book as thoughtful as Smith’s.

[1] Even if a model makes sense, you need to make sure it continues to do so. Investment approaches can become over utilized and ineffective when they attract too much capital. See here and here.

↧

Factor Investing: Implementation Costs Do Matter

November 26, 2017, 11:49 am

≫ Next: Common Misconceptions About Momentum

≪ Previous: Book Review: Standard Deviations, Flawed Assumptions, Tortured Data and Other Ways to Lie with Statistics

Progress in science comes when experiment contradicts theory. – Richard Feynman

One of the tenets of modern portfolio theory is that you cannot generally beat the market after transaction costs. Yet academic researchers have shown that momentum consistently beats the market. Other factors besides momentum have also cast doubt on the efficacy of the efficient market hypothesis.

There is one way though that academics can still hold on to the efficient market hypothesis. It is to show that academic research on anomalies does not hold up in the real world after accounting for transaction costs.

Chen, Stanzl & Watanabe (2002) were the first to explore the price impact of large-scale factor investing. They concluded that the maximal fund sizes for factor-based anomalies, especially momentum, to remain profitable are too small. Lesmond, Shill & Zhou (2003), Korajczyk & Sadka (2004), Fisher, Shah & Titman (2015), Novy-Marx & Velikov (2015), Beck, Hsu, Kalesnik & Kostka (2016) all came to similar conclusions.

Fund sponsors who jumped on the factor bandwagon were not happy to see these results. Like what happens when drug companies have academics do trials of their products, fund sponsors had their own researchers look at the capacity of factor-based strategies.

Frazzini, Israel & Moskowitz (2014) work for AQR. They analyzed 16 years of proprietary data ending in 2013. They showed scalable results over this period. But the number of funds using momentum has increased substantially since then.

Ratcliffe, Miranda & Ang (2016) work for Black Rock. They also contend there is enough capacity for scalable results managing factor-based funds using their high frequency trading market data.

Some argue that trading costs are not an issue by pointing to similar performance between momentum based funds and the momentum indices these funds track. The problem with that argument is the indices themselves may suffer from the prior impact of trading costs on the stocks that make up those indices. Chow, Li, Pickard & Garg (2017) show that market impact costs can come from trading at temporarily inpacted prices during index rebalances. This means market impact costs are hidden and cannot be seen in a direct comparison between fund performance and index performance.

The main drawback of all these studies, whether academic or industry based, is that they depend on assumptions about future transaction costs and market liquidity. Assessing implementation costs using parametric transaction cost models may be incomplete or misguided. No one can say with any degree of certainty what the future will bring. Ratcliffe et al. acknowledge this when they say, “The exercise we conduct in this paper is hypothetical and involves several unrealistic assumptions.”

Real World Results

It is not uncommon for academic finance theories to not hold up in the real world. The capital asset pricing model (CAPM) is a good example of that. In past blog posts here, here, and here, we highlight some factor research using actual fund results. One study we cite by Loughran & Hough (2006) compares the past performance of value versus growth funds. After examining mutual fund performance from 1965 to 2001, they concluded that superior long-run performance from value is an “illusion.”

A second study is by Arnott, Kalesnik & Wu (2017). They applied two-stage Fama-MacBeth regression to the last quarter-century of mutual fund returns. They showed the real-world return for the value and market factors to be half or worse than theoretical factor returns. On a real-time basis, the momentum factor provided no benefit whatever.

New Study

Two Duke professors, Patton & Weller (2017), recently came out with a study of real versus theoretical performance of momentum, value, and size factors called “What You See Is Not What You Get: The Costs of Trading Market Anomalies.” Their work reviews prior studies of real world factor capacity and corrects some shortcomings of the Arnott et al. study.

The authors start with a two-stage Fama-MacBeth regression applied to 7320 U.S. domestic mutual funds from January 1970 to December 2016. In the first stage they determine the estimated factor loadings for each fund. In the second stage, they regress the excess returns of all funds against the estimated factor loadings to get the factor premia earned by each fund. They then compare these to the theoretical factor returns.

Implementation Costs

Their approach differs from the Arnott et al. one by focusing more explicitly on implementation costs. They make improvements in the way Fama-Macbeth regression is used. From 1970 through 2016, the authors find that annual implementation costs range from 2.2% to 8.5% for momentum strategies. This makes momentum profits inaccessible to typical asset managers, according to the authors.

For value, the authors come up with annual implementation costs of 2.6% to 5%. They report overall that “after accounting for implementation costs, typical mutual funds earn low returns to value and no returns to momentum.”

Implementation costs for both value and momentum are stationary throughout this period. The authors say industry inflows offset declines in bid-ask spreads and commissions.

In addition to Fama-MacBeth regression, the authors use a second approach called matched pairs analysis. Here they directly compare the compensation for stocks to mutual funds with similar characteristics. They sort stocks into quintiles and match them up with three mutual funds closest to them in factor beta. This is a more direct approach than Fama-MacBeth regression.

Their Fama-Macbeth approach shows that implementation costs erode almost all the return to value and momentum strategies of mutual funds. But there is little impact on market and size strategies. Matched pairs analysis shows comparable performance attrition for value and momentum strategies. But it also shows high costs to trading small stock portfolios.

In summary, the authors say the implementation gap is large and statistically significant for all the factors they examine. None of the factor strategies earn returns after real-world costs during the 1970 to 2016 period.

In future research, the authors say they will apply these same tools to other residents of the factor zoo (such as low volatility and quality) and see how they survive in the wild.

Implications

I wrote my first momentum paper in 2011. It was called “Optimal Momentum: A Global Cross Asset Approach.” [1] I looked at momentum applied to stocks, industries, investment styles, and geographic equity markets. I found that momentum worked best when used with geographically diversified stock indices.

In 2015, Geczy & Samonov (2015) applied momentum to U.S. stocks, global sectors, country equity indices, government bonds, currencies, and commodities. Looking at the past 215 years of data, they came to the same conclusion as I did. Momentum works best when applied to geographically diversified stock indices.

Neither my study nor Geczy & Samonov’s study took into account implementation costs, which would have made our equity index results even stronger compared to stocks. Implementation costs are substantially lower when momentum is applied to stock indices rather than to individual stocks.
Yet almost every momentum fund applies momentum to individual stocks rather than to country or regional stock indices.

Studies such as those by Arnott et al. and Patton & Weller may eventually get fund sponsors to pay attention to the implementation cost evidence with stock-based factor investing. But I would not count on it anytime soon. The growth of factor- based investing has been explosive and is expected to continue that way.

Continued growth in factor-based investing could very well aggravate the scalability problem associated with high implementation costs. Ignoring implementation costs, there is still widespread belief that factors can enhance return or decrease risk.

The same behavioral biases that make momentum effective may prevent financial professionals and investors from recognizing important information regarding implementation costs. These include anchoring, slow diffusion of information, and herding. This situation may not change until years from now when investors compare the actual performance of factor-based funds to their appropriate benchmarks.

[1] Second place winner of the 2011 Wagner Award for Advances in Active Investment Management given annually by the National Association of Active Investment Managers (NAAIM).

↧

Common Misconceptions About Momentum

April 9, 2018, 7:03 am

≫ Next: The Evolution of Investing

≪ Previous: Factor Investing: Implementation Costs Do Matter

Momentum is one of the most researched topic in financial market literature. A search of the SSRN database on momentum will turn up around 1000 papers written over the past three years and 3000 papers in total.

With so much information available, it is not be surprising that many analysts have missed seeing some of the research. Based on the way momentum is generally used, it is clear to me that there are some serious misconceptions about it. Here is my discussion of some of the more serious ones.

1)"Momentum is best used with stocks."

Initial academic research on momentum by Jegadeesh and Titman (1993), Asness (1994), and others focused on U.S. stocks. This explains why momentum was at first associated with stock investing.

But to see if momentum was robust, researchers soon applied it to other markets. Momentum was found to be effective not only with U.S. stocks. It also outperformed with international stocks, industry groups, stock indices, bonds, real estate, commodities, and currencies.

When I started to do my own momentum research in 2010, I looked at it from a practical point of view. I tried to determine how one might best use it. I applied momentum to U.S. stocks, industry and style groups, and world regional stock indices. In 2011, I wrote a paper called “Global Momentum: A Global Cross Asset Approach.” It showed that momentum worked best with regional stock indices.

In 2015, Geczy and Samonov did a more comprehensive study called “Two Centuries of Multi-Asset Momentum (Equities, Bonds, Currencies, Commodities, Sectors, and Stocks).” They looked at momentum with country indices, government bonds, currencies, commodities, sectors, and U.S. stocks back to 1801. Momentum gave significantly positive results in all areas. Like my results, they found momentum worked best with geographically diversified stock indices.

Below is an example showing geographic stock index momentum. About half the capitalization of global equities is in U.S. stocks. The other half is, of course, in non-U.S. stocks. We will compare the performance of the S&P 500, representing U.S. stocks, to the MSCI ACWI ex-US, representing the rest of the world.

Each month we invest in whichever of the two has had better performance during the past 12 months. We use a 12-month look back because that was found to work by Cowles and Jones in 1937. It has been used in research ever since then. We need not worry about selection bias since we are using all areas of the world. Data mining is not an issue, since we are using a long-established model parameter. There averages less than one trade per year. So trading impact is not an issue. Here are the results from when non-U.S. stock index data became available.

1/1971 to 3/2018	S&P 500	ACWI x-US	50/50	MOMENTUM
CAGR	10.7	10.3	10.7	12.3
Annual Std Dev	15.1	17.2	14.6	15.7
Sharpe Ratio	0.42	0.36	0.45	0.55
Worst Drawdown	-51.0	-57.4	-54.2	-54.6

Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Positions are rebalanced monthly. Please see our Disclaimer page for more information.

This simple momentum model is always invested in stocks, so there is minor tracking error.There are no trading impact issues.

This strategy shows an increase in annual return of 160 basis points versus both the S&P 500 and a 50/50 U.S./non-U.S. allocation. Most investment managers would be happy over the long-run to outperform the market by 160 bps annually with comparable risk. Yet no one, as far as I know, is using this simple strategy.

Neither my nor Geczy and Samonov’s research took into account the price impact of trading. There is considerable controversy in this area with respect to individual stocks. See here for details.

The most recent paper on the subject is by Patton and Weller (2017). In “What You See Is Not What You Get: The Costs of Trading Market Anomalies,” they review previous studies. Their use two different methods to do their own research on the trading impact of momentum on stocks. They conclude: “Our estimates… imply that implementation costs erode almost the entirety of the return to value and momentum strategies... momentum profits, in particular, may be out of reach for the typical asset manager.”

Despite the issue of price impact and the documented superiority of momentum used with geographic indices, nearly all momentum funds use momentum with stocks. One cannot help but wonder why this is. It is likely for the very same reasons momentum works. These include the slow diffusion of information (research results), anchoring to prior beliefs, and underreaction to new information. My experience leads me to think there is also an irrational preference for stocks over indices.

2) "Momentum is best used on a relative strength, cross-sectional basis."

There is now plenty of research on cross-sectional, relative momentum that compares assets to one another. All momentum research between 1993 and 2010 was of this type. It was not until 2012 that Moskowitz, Oii and Pedersen released a paper called “Time Series Momentum.” In early 2013, I came out with a paper called “Absolute Momentum: A Simple Rule-Based Strategy and Universal Trend Following Overlay.” These papers established absolute (time-series) momentum as another type of momentum.

Absolute momentum is a form of trend following based on autocorrelation. It assumes assets that have been strong over the recent past will continue to be strong.

Absolute momentum is as universal and consistent as relative momentum. Using at least 25 years of data applied to equity index, currency and commodity futures, Moskowitz et al. showed persistence in return for absolute momentum. They found it performed best in extreme markets and had little exposure to standard asset pricing factors. This gives it considerable value as a portfolio diversifier.

My paper applied absolute momentum to stock index, bond index, real asset, stock/bond balanced, and risk parity portfolios. It showed that absolute momentum can identify regime change and add value as both a stand-alone strategy and a portfolio overlay.

In their 2012 paper “A Century of Evidence on Trend-Following Investing,” Hurst, Oii, and Pedersen applied absolute momentum to equity indices, bond indices, commodity futures, and currencies. They found it was consistently profitable across decades ever since 1903.

The amount of research done on absolute momentum has been catching up with relative momentum. If you do a search on “time series momentum” (the preferred academic term) on SSRN, you will find over 200 papers. You might wonder now after all this research whether relative or absolute momentum gives better results.

Bird, Gao, and Yeung (2017) in their “Time Series and Cross-Sectional Momentum Strategies Under Alternative Implementation Strategies” applied relative and absolute long/short momentum to stocks in 24 developed markets from 1990 to 2012. They found positive returns from both forms of momentum under alternative implementations. But they concluded that “time-series momentum is clearly superior,” and “momentum is best implemented using time-series momentum.”

D’Souza, Srichanachaichok, Wang, and Yao (2017) in their “The Enduring Effect of Time-Series Momentum on Stock Returns Over Nearly 100-Years”, studied long/short absolute momentum in U.S. stocks from 1927 to 2014 and in international stocks since 1975. They found absolute momentum could entirely account for relative momentum. Absolute momentum performed well in both up and down markets. Unlike relative momentum, absolute momentum did not suffer from January losses and market crashes.

Despite the evidence in favor of absolute momentum, almost every momentum fund uses just relative momentum. The reasons for this may be the same as to why investors prefer momentum with stocks instead of indices – the slow diffusion of research results and anchoring that overweights prior information. But there may also be other reasons. First, there is a long-standing bias against tactical approaches such as trend following. What many don’t realize is all momentum is a form of trend following. Relative momentum looks at trends between assets. Absolute momentum looks at the trend of an asset itself over time

Another reason absolute momentum has not been well received may be its tracking error, especially during bull markets. Absolute momentum is known to outperform in bear markets. But in bull markets whipsaw losses and trading lags can constrain the performance of absolute momentum portfolios. That is why some advisors use trend following only for satellite positions within diversified portfolios.

There is no way to eliminate all tracking error. But I show in “Why Does Dual Momentum Outperform” how dual momentum can lead to superior long-run performance in both bull and bear markets. Relative momentum can boost returns during bull markets. This can compensate for the whipsaw losses and performance lags of absolute momentum. Absolute momentum can reduce the downside exposure of relative momentum during bear markets. In my 2012 paper, I introduced the concept I called dual momentum.

Based on the evidence above, if I had to choose between relative and absolute momentum, you would most likely choose absolute momentum. But there is no reason you cannot use both. D’Souza et al. looked at both forms of momentum, as well as dual momentum. They found that dual momentum applied to long-short stock portfolios generated striking returns of 1.88% per month.

3) "Momentum (trend following) is not as reliable as diversification in reducing risk."

The first thing to understand is that momentum is all about diversification. Momentum diversifies by time as well as by asset class. This makes it adaptive to changing market conditions. Traditional fixed diversification creates a drag on performance from poorer performing assets. Momentum reduces performance drag by being only in assets that are performing well.

Here is an example comparing the performance of momentum versus a diversified fixed portfolio. For the fixed portfolio we will use Ivy 5 developed by Meb Faber. Ivy 5 holds equal size positions in indices of U.S. stocks, foreign developed stocks, intermediate bonds, REITs, and commodities.

For momentum, we will use the Global Equities Momentum (GEM) model featured in my book. GEM uses 3 assets and holds only one at a time. It decides whether to be in stocks or bonds based on absolute momentum. When stocks are selected, it chooses either the S&P 500 or the MSCI ACWI ex-U.S. based on relative momentum.

Stocks have the highest risk premium of any asset class. That is why we want to be in them as much as possible, providing their trend is positive. As you can see, dual momentum has done a much better job in reducing tail risk and improving risk-adjusted returns.

1/1973 to 4/2018	IVY 5	GEM
CAGR	9.4	16.1
Annual Std Dev	11.0	12.2
Sharpe Ratio	0.40	0.89
Worst Drawdown	-47.3	-17.8

There are some who criticize trend following and systematic trading approaches because of what they call "timing luck". With some strategies, results vary a lot depending on what day of the month you rebalance positions. This uncertainty causes them to trade portions of their portfolios at different times during the month (tranching). It also leads them to doubt the efficacy of systematic trading and put more emphasis on traditional diversification

Tranching may have merit in some situations. But that is not always the case.

Swinkels and van Vliet (2010) showed that stocks exhibit a statistically significant turn-of-the-month effect. The last trading day of the month and first few trading days of the next month outperform other days. This may be due to institutional portfolio rebalancing at or just before month-end. This rebalancing replaces poor performing stocks with better performing ones. It is sometimes called "window dressing". Momentum means persistence in performance. We want to be in portfolios after better performing stocks have replaced the laggards. This lets momentum work in our favor.

Our dual momentum models, which are most of the time in stock indices, perform better by rebalancing on the first or second trading day of the month. This lets us exploit the turn-of-the-month momentum effect to our advantage.

AllocateSmartly is a service that tracks systematic trading models. Their reporting of our GEM model understates its performance by 90 basis points annually. This is because they substitute the MSCI EAFE index for the broader MSCI ACWI ex-US index that we use. But AllocateSmartly did an analysis of GEM that has some value. They evaluated GEM performance over the past 363 months based on which normalized trading day of the month one uses to rebalance positions.

Source: www.allocatesmartly.com

You can see here that the first and second trading days of the month have the highest Sharpe and Sortino ratios. These days are consistent with the turn-of-the month effect. We use them for our portfolio rebalancing.

If investors and advisors studied more of the literature on momentum and trend following, they would surely be impressed. There is nothing else that comes close in terms of favorable results and longevity.

Besides the research mentioned above, here are a few other studies reinforcing these points. Clare et al. (2014) in “Size Matters: Tail Risk, Momentum, and Trend Following in International Equity Portfolios” look at 20 developed and 12 developing countries from 1995 through 2013. They find limited evidence for the outperformance of relative momentum stock portfolios. Trend following though is observed to be a very effective strategy delivering superior risk-adjusted returns.

Geczy and Samonov (2015) show the effectiveness of absolute as well as relative momentum on 200 years of data. Lemperiere et al. (2014) in “Two Centuries of Trend Following,” look at trend following across commodities, currencies, stock indices, and bonds since 1800. They find “the existence of trends one of the most significant anomalies in financial markets.” Their results were very stable across time and asset classes.

For those wanting even more history, Greyserman and Kaminsky (2014) in Trend Following with Managed Futures take trend following back 800 years! When applied to 84 different markets, absolute momentum showed a Sharpe ratio of 1.16 versus 0.47 for buy-and-hold. The worst drawdown of trend following was 25% lower than buy-and-hold. The duration of its longest drawdown and the average duration of the longest five drawdowns were 90% and 80% shorter. They found trend following to have a low correlation with traditional asset classes, interest rate regimes, and inflation. It has also provided consistently positive performance during crisis periods.

Conclusion

The above are serious misconceptions about momentum that I hope I have cleared up. Feel free to pass along this information to all who might benefit from it. Those who neglect momentum and trend following are missing out on opportunities that one could only dream about in days past.

↧

The Evolution of Investing

June 28, 2018, 6:29 am

≫ Next: Momentum Solutions for Retirement

≪ Previous: Common Misconceptions About Momentum

I began my investment career in 1974. In 1976 I left a large retail brokerage firm to join a premier investment bank. I had both retail and institutional clients. So I had a well-rounded knowledge of Wall Street.

The 1970s was soon after the dark ages of investing. Modern portfolio theory existed in the academic but not the real world.

Looking at how investing has progressed since then, I thought it would be interesting to trace its evolution both in theory and in practice. Perhaps we can learn something from the past.

1960s and earlier

Modern portfolio theory began with Markowitz’s work in the 1950s. He called this approach mean-variance optimization (MVO). Markowitz used quadratic programming to construct efficient portfolios with past returns and covariances as inputs. Efficient portfolios have the least amount of variance at a specified level of expected return or the highest expected return at a specified level of volatility.

The problem with Markowitz’s approach is it did not work well in practice. This is because model inputs, especially past returns, are not stable going forward. Errors are multiplicative and become large due to the quadratic nature of the MVO model. At first, no one knew this problem existed because implementing MVO was impractical then. Matrix inversions with thousands or even just hundreds of stocks challenged computing capabilities at that time.

In the 1960s researchers simplified Markowitz’s approach by developing the Capital Asset Pricing Model (CAPM). This was an elegant solution to the portfolio allocation problem using a linear model. Researchers found they could regress assets against the S&P 500 to create “beta.” You could determine your risk exposure by looking at the beta of your portfolio. Beta became king in academic finance and with institutional investors. It was used for portfolio construction and capital budgeting. It’s corollary, “alpha”, was used for institutional performance evaluation. All assets and portfolios was characterized solely by their performance relative to the S&P 500 index.

Institutions owned less than 10% of U.S. equities back then. Retail brokerage offices were ubiquitous. Brokers pitched stories about companies to clients who paid high fixed brokerage commissions. When recommended stocks went up, clients were encouraged to lock in profits. When stocks went down or were unchanged, clients were urged to sell and buy more promising ones.

Most brokerage accounts were under diversified and over traded. Brokers recommended glamour stocks to aggressive investors. They touted bonds or defensive stocks, like utilities, to conservative investors. Blue chips were core holdings for most investors.

1970s

Even though fixed commissions ended in 1975, most brokerage firms kept their commission costs high. They figured investors would pay them to get recommendations and to keep their accounts at big-name brokerage firms. Discount brokerage firms were largely unknown.

The first public index fund was started by Vanguard in 1976 with $11.3 million. Many called it “Bogle’s Folly.” Malkiel (1995) and Fama and French (2009) showed that active funds underperform the market by the amount of their fees.

But most investors were unwilling to settle for being average. By competing against each other using the same information, investor returns were not average. After costs and fees, they were below average.

Some academics began to question the supremacy of single-factor beta. Barr Rosenberg, a Cal finance professor, setup BARRA as a consulting firm to institional investors. BARRA looked at 20+ additional items when doing linear modeling. Their "extra-market covariances" were the precurser of today's factor-based investing.

Institutional participation in stocks was still low then. The public continued to rely on brokerage firms and their recommendations.

1980s

In the 1980s more academic ideas began to filter into the marketplace. Sophisticated investors recognized the need for a portfolio of at least 30 well-diversified stocks to reduce idiosyncratic risk. Mutual funds that performed well garnered attention. Peter Lynch’s no-load Magellan Fund attracted a large following. Many other funds had front-end loads to compensate selling brokers. Investments were promoted and sold to existing clients back then more than they were bought by informed investors.

Research departments began to pay attention to international diversification after academics showed that it reduced portfolio variability. Beta was used more frequently as a measure of market exposure.

1990s

In the 1990s there was more recognition of modern portfolio theory. This meant more interest in the efficient market hypothesis and index funds. CAPM expanded to include size and value as additional factors. This was soon followed by momentum.

Discount brokers became accepted, no-load funds gained market share, and institutional ownership of equities expanded. But with average equity returns of 17% per year in the 1980s and 1990s, most investors were reluctant to change how they actually invested. It was still a stock pickers world, but methodical asset allocation began to get more attention from institutional investors.

We began to see more "closet index" funds. These closely tracked passive indices but charged active management fees that adversly affected performance. These number of closet index funds has continued to grow since then.

2000s

Investor attitudes changed dramatically in the 2000s due to the severe 1999-2000 and 2008-2009 bear markets. Many investors abandoned equities. Some never returned. There was more focus now on risk exposure. Alternative investments, such as commodities and hedge funds, attracted both institutional and individual investor interest. Advisors stressed greater diversification, often in the form of Global Asset Allocation (GAA). Meb Faber’s popular IVY 5 portfolio was an example of that. Pricing models expanded to include additional factors to better explain asset returns.

2010s

Indexing has continued to attract followers. But it still resides in the shadow of active management. According to Morningstar, only 19% of the U.S. stock market is owned by index funds. Thirty percent of mutual funds, 15% of institution investing, and 5% of global investing use low-cost indexing. It is surprising there has not been more interest in indexing given that 83% of mutual funds underperformed the market over the prior 10 years.

The latest “improvement” on index funds is factor investing. Factors (and related smart beta) are based on quantitative analysis of past data. Investors are receptive to factor investing because they think it may offer them better returns than an index fund at a lower cost than traditional active management.

Investors often also think complicated approaches are better than simpler ones. The opposite is actually true. Simpler approaches are less likely to suffer from data snooping, selection bias, and overfitting issues. Complicated approaches are not as viable as simpler methods, but investors will pay up for them.

As with commodities and hedge funds in the 2000s, the performance of factor-based investing has been disappointing since they became popular. The size factor has failed to provide an advantage ever since it was introduced in 1982. Back in 2006 Loughran and Hough presented evidence that cast doubt on the efficacy of value fund investing.

Yet many advisors and investors ignore or dismiss real-world factor performance. They still reference theoretical results. Those rely on data before factors were widely used and their trading impacted asset returns. See here for more on the issues associated with factor investing.

2020s

What can we expect to see in the future? Indexing should continue to grow. Early this year John Bogle said index funds took in $3.3 trillion in net cash flow over the past 10 years. Active funds took in just $150 billion, or only 5% of total industry cash flow.

Trend following is now getting plenty of attention from academics. See here for more on this. Below is Meb Faber’s IVY 5 GAA (Buy and Hold) portfolio with the addition of a trend filter to create a Global Tactical Asset Allocation (GTAA) portfolio.

                               Source: Faber (2007), “A Quantitative Approach to Tactical Asset Allocation”

Entrenched investment prejudices are hard to overcome. Many still think small stocks outperform larger ones on a risk-adjusted basis.

Most of us are used to looking for bargains. We think value investing represents a bargain. But we may not be factoring in all the risks associated with value.

Institutional investors and fund sponsors are subject to the same prejudices as everyone else, and maybe even more so. They have been exposed to factor research since 1992 or earlier. At least 16 multi-factor funds were set up duriing the past two years. All of them included the value factor, and one-quarter included the size factor.

Tactical approaches were looked upon as “voodoo” for many years by academics and academically trained investment professionals. Institutional investor prejudice in the opposite direction may make it difficult though for trend to become widely accepted. This is reinforced by the fact that institutional investors continue to have a dominant influence on investing. Individuals now hold only $4 trillion of the $27 trillion invested in U.S. stocks.

This is actually good news for those of us who use trend as a component of our investment models. Reluctance to accept trend should lessen the chance of crowding out future returns.

Conclusions

There are things we can learn from the history of investing:

1)    It is not easy to beat the market after accounting for risks, costs, and fees

Markets are highly competitive, so it hard to find an edge. There are worse things you can do than buy a passive index fund. Warren Buffett instructed his heirs to put 90% of their inheritance in an S&P 500 index fund and 10% in short-term U.S. debt instruments.

2)    Costs are important and easily controllable

Vanguard estimates that paying 110 bps (the average active management fee) over 30 years would erode a portfolio’s market value by 25%. All investors should pay close attention to their investmment costs. That is the simplest and easiest way to create alpha.

3)    Diversification is still the closest thing to a free lunch

When you invest in multiple assets, your expected return is the weighted average of those asset returns. But portfolio volatility is reduced if those assets are less than perfectly correlated to one another. Diversification can also help mitigate the uncertainty of assets continuing to perform in the future as they have in the past. The downside of traditional diversification is that assets having a lower expected return create a drag on portfolio performance.

Closely associated with trend is the idea of temporal diversification where you invest in the strongest assets over time. This is also the same as relative strength momentum investing. Weaker assets do not create a drag on portfolio performance because you do not invest in them until their performance improves. Using temporal diversification, portfolios can have a higher return than the weighted average return of its individual assets. Relative strength momentum is a more intelligent way to diversify.

4)   Don't be firmly attached to your investment beliefs

As we have seen, investment ideas evolve over time. CAPM was once regarded as sacrosanct. Fisher Black (who would have received the Nobel Prize had he lived a few more years) said of CAPM, “The theory is right. It just doesn’t work.” The same is true of MVO. Portfolio insurance is another concept born in academia that failed miserablty in practice. Investors should not be too surprised if today's popular investment approaches do not live up to their expectations.

What should we do then? First, we can keep up on financial research. We should also remain flexible enough to change our approach based on evidence, not convention. If we are unwilling to do these two things, we could do a lot worse than investing in low-cost passive index funds.

↧

Momentum Solutions for Retirement

July 31, 2018, 8:37 am

≫ Next: Back to Fundamentals

≪ Previous: The Evolution of Investing

by Matt Richardson, JD, PhD

As the surge of boomer retirements continues, commentators have given new thought to what safe withdrawal rates are for retirement accounts. The topic is especially significant given two additional factors. First, retirement balances are shockingly low for boomers (Ghilarducci 2015)[1]. Second, market fundamentals do not suggest that either bonds or equities will generate reliably strong rates of return over the next 10-15 years on a buy-and-hold basis (Research Affiliates 2018)[2]. But momentum portfolios as set forth in Gary Antonacci’s book Dual Momentum (2014) may provide a constructive solution to these problems.

Historically, a simple 4% rate has been perceived as a safe withdrawal rate (SWR) for retirement accounts. This includes adjustments for inflation while assuming thirty years of retirement under various portfolio allocations of stocks and bonds managed on a buy-and-hold basis (Bengen 1994)[3]. More recently, however, commentators have questioned whether a 4% withdrawal rate is too risky under current and foreseeable market conditions (Miller 2017)[4]. Overvalued equities and lower expected returns on bonds in a rising interest rate environment are the chief concerns.

But the vast majority of SWR studies are based on various allocations of buy-and-hold portfolios of stocks and bonds. Researchers have only recently begun to consider how the SWR calculus could be affected by factor-based investment styles like value or momentum (Kitces 2016[5] ; Miller 2017[6]).

As for momentum, Andrew Miller has suggested that including a 10% allocation to a managed futures investment strategy, based on trend following or absolute momentum principles, could increase the withdrawal rate for those contemplating retirement. Besides functioning as a diversifier to buy-and-hold portfolios of stocks and bonds, managed futures can come to the rescue in times when stocks and bonds both underperform, thus generating “crisis alpha” (Miller 2016)[7].

Miller’s suggestion of using trend-following as a means of increasing SWRs is worth a closer look. If a 10% allocation to an absolute momentum system can boost SWRs in an otherwise buy-and-hold portfolio of stocks and bonds, what could the SWR be if a greater allocation to a momentum system were utilized?

Several British researchers (Clare, et al. 2016) in “Sequencing, Perfect Withdrawal Rates and Trend Following Investing Strategies: Making the Known Unknown Less Unknown”sought to determine a “perfect withdrawal rate” (PWR) for a portfolio using different investment strategies over a 20-year time frame, including buy-and-hold, valuation-based, and trend-following strategies. According to the authors, the PWR would be the correct withdrawal rate for investors who had perfect knowledge about how their investments would perform over the ensuing 20 years.

The authors’ trend-following strategy was tested on monthly S&P500 data from 1872 to 2014 rebalanced monthly, depending on whether the price of the index exceeded the 10-month moving average of the current price on the rebalance date. Because the portfolio consisted of one asset, the authors’ strategy was a pure trend-following approach lacking any element of relative strength momentum.

The annualized real return of their strategy was 8.84%, more than 2% greater than the index itself during this time frame, and with substantially lower volatility (9.86% versus 14.29%) and drawdowns (34.88% versus 76.80%), all characteristic of trend-following strategies. The trend-following strategy largely avoided one form of “sequencing risk,” or the risk of a sharp decline in the value of the portfolio early in the distribution phase, even when distributions began in the midst of a bear market.

By utilizing this data set with 20,000 Monte Carlo simulations, the minimum PWR, specifically that in the 5^th percentile or less, was from 5.57% to 6.61% real, while the index itself could only provide a PWR of only 2.95% to 4.2%. The authors concluded that a simple trend-following system “dampens volatility, typically maintains or increases returns over longer periods, and substantially reduces the maximum drawdown for that series.”

Similar results hold true when SWRs are calculated from extensive performance data included in Gary Antonacci’s Global Equities Momentum (GEM), a monthly dual momentum system that includes both relative strength and absolute momentum. In Dual Momentum, Antonacci’s GEM data covers 1974 and 2013, a period that includes part of one severe bear market with high inflation (1973-74) and two full bear markets (2000-2002; and 2007-2009). Even more extensive GEM data is provided on Antonacci’s website, from 1971 to the present (2018)[8].

Notably, the annualized returns from 1974-2013 exceeded 17% with a maximum drawdown of just under 18% with only four losing years and no worse year than an 8.2% annual loss. With such a favorable risk-reward profile, Antonacci’s GEM system can comfortably sustain higher safe withdrawal rates on 2500 Monte Carlo runs of 30-year periods (Table 1). Note that Monte Carlo analysis is particularly useful for retirement withdrawal strategies because it directly accounts for sequencing risk by repeatedly changing the order of returns.

Table 1
Withdrawal Rate Risk on GEM data 1971-2017 (2500 Monte Carlo simulations of 30-year sequences; no compounding)
W/D Rate	Risk of Ruin: Drawdown risk to 50% or less of initial capital	Median Drawdown	Median Return	Probability of surplus greater than initial investment after 30 years
3.4%	0%	9.7%	134%	97%
4.0%	0%	11.0%	132%	98%
5.3%	0%	14.0%	133%	98%
6.0%	0%	15.3%	133%	97%
8.0%	0%	18.9%	133%	97%
9.6%	1%	21.9%	132%	97%
12.0%	5%	25.5%	132%	94%

Under every withdrawal rate scenario tested, from 3.4% to 12%, the median portfolio managed to more than double the initial investment after 30 years despite withdrawals of various sizes. Further, as a “risk of ruin” measure, only when the withdrawal rate approached or exceeded 10% did any risk at all appear that the account balance would dip below half of the original amount invested at any time within the 30 years.

Of course, one counter-argument to using Antonacci’s GEM data, and one that would apply to using any historical market data at all, is that the markets will not necessarily be as favorable to trend-following strategies in the future as they have been over the GEM data time frame. Leaving aside the possibility that future markets may even be kinder to trend-following strategies than they have been—also a possibility—it may be worthwhile to consider what SWRs could be for weaker retirement scenarios using GEM data (Table 2).

Table 2
Withdrawal Rate Risk on GEM data 1971-2017 Using the weakest 30 years of data (2500 Monte Carlo simulations of 30-year sequences; no compounding)
W/D Rate	Risk of Ruin: Drawdown risk to 50% or less of initial capital	Median Drawdown	Median Return	Probability of surplus greater than initial investment after 30 years
3.4%	0%	25.3%	(45.3%)	25%
4.0%	0%	29.8%	(46.8%)	24%
5.3%	4%	39.1%	(47.8%)	25%
6.0%	9%	42.8%	(47.1%)	26%
8.0%	33%	56.0%	(52.2%)	23%
9.6%	49%	61.1%	(67.8%)	23%
12.0%	67%	54.8%	(50.7%)	21%

The data included in this analysis consists of only the weakest 30 years in the 1971-2017 GEM data set, eliminating the two best years of the 1970s since 1971 (1972 and 1976), all but three years of returns from the bullish 1980s (1982-1987; and 1989), the entirety of the period from the bull market of 1995-1998, the two best years of the 2000s (2003 and 2006), and the best year of the 2010s (2013). At the same time, this data set contains data from all bear markets during the period from 1971-2017, including 1973-1974, 2000-2002, and 2007-2009.

Despite these adjustments, the nominal compound annual growth rate of this data set remains 9.24%, slightly more than half of the annualized rate of return of the full GEM data set. At the same time, the volatility is reduced from 12.6% to 8.3%. Otherwise, the maximum drawdown increases slightly from 17.8% to 18.9%.

Even with substantially weaker performance data, a withdrawal rate of 5-6% is relatively safe, again because of the reduced volatility and drawdowns that trend-following provides. Under these circumstances, a 5-6% withdrawal rate carries with it the following: a greater than 90% chance that the bottom half of the initial capital invested is never breached, a median loss of less than half of the original balance after 30 years of withdrawals, and a one-in- four chance that the portfolio is larger after 30 years than from when withdrawals began.

For these reasons, it is not surprising that those who study SWRs when applied to buy-and-hold portfolios of stocks and bonds would term a 5% withdrawal rate as “risky” and a 6% or greater withdrawal rate as “gambling” (Bengen 1994). But investment strategy matters, and buy-and-hold is not the only option. Momentum and trend-following approaches create conditions necessary for higher SWRs. These include expected comparable or higher annualized returns, lower volatility, and substantially lower drawdowns. Even under far less than optimal conditions, a likely larger SWR rate continues to apply to a momentum or trend-following investment strategy than the 3-4% rate suggested for buy-and-hold strategies.

For those recent and prospective retirees with lower account balances, utilizing a dual momentum strategy like Antonacci’s GEM system, instead of buy-and-hold, could make all the difference. This could be especially true in the current market environment which includes the prospect for lower returns on buy-and-hold portfolios. Many investors could benefit from incorporating some element of momentum strategies into managing their retirement accounts, whether to accomplish a higher withdrawal rate or simply to reap the well-established benefits of momentum and trend following for other purposes.

Matt Richardson is Director of Litigation at the law firm of Manley Deas Kochalski LLC based in Columbus, Ohio.

[1] http://www.economicpolicyresearch.org/images/docs/retirement_security_background/Inadequate_Retirement_Account_Balances_for_Families_Nearing_Retirement.pdf
[2] https://interactive.researchaffiliates.com/asset-allocation.html#!/?currency=USD&model=ER&scale=LINEAR&terms=REAL
[3] http://www.retailinvestor.org/pdf/Bengen1.pdf

[4] https://alphaarchitect.com/2017/11/08/trend-following-managed-futures-safe-withdrawal-rates/
[5] https://www.kitces.com/blog/should-equity-return-assumptions-in-retirement-projections-be-reduced-for-todays-high-shiller-cape-valuation/
[6] https://alphaarchitect.com/2017/11/08/trend-following-managed-futures-safe-withdrawal-rates/
[7] https://alphaarchitect.com/2016/08/24/managed-futures-understanding-a-misunderstood-diversification-tool/
[8] http://www.optimalmomentum.com/gem_trackrecord.html

↧

Back to Fundamentals

July 20, 2015, 11:45 am

≫ Next: Perils of Data Mining

≪ Previous: Momentum Solutions for Retirement

After winning two consecutive national championships, the Green Bay Packers lost a game due to sloppy play. Coach Lombardi called a meeting the next day to get his team back to fundamentals. When all the players were assembled, Lombardi held a football high up in the air and declared, “Gentlemen, this is a football!” From the back of the room, running back Paul Hornung shouted back, “Coach, can you slow down?”

Sometimes we all need to be reminded of fundamentals. The fundamental goal of investing should be to receive the most gain with the least pain. The question then becomes, how do we achieve this?

Asset Returns

Over the past 100+ years, stocks have provided more than three times the real return of bonds despite the strong bond market of the past 35 years. A much higher long-run return from stocks makes sense, since stocks are riskier than bonds. They should therefore compensate investors with a higher risk premium.

The following chart by Wharton professor Jeremy Siegel shows this same dynamic over 200 years from the years 1802 to 2012.

Source: Stocks for the Long Run, 5th edition, Jeremy Siegel

Again we see that over the long-run, stocks have earned the highest return by a large margin. The annualized real return of U.S. stocks has been nearly twice as high as the annualized return of bonds since 1802.

Drawdowns

Even though returns are maximized, the problem with holding only stocks in one's portfolio is their high volatility and negative skewness. These create considerable left tail/drawdown risk. There have been two bear market drawdowns in U.S. stocks greater than 50% during just the past 15 years.

Not only can large equity erosions create discomfort and uncertainty in the minds of investors, but they can cause investors to react in ways that are counter to their own best interests. The yearly Dalbar studies show that investors underperformed the funds they were invested in by an average of 4% annually over the past 20 years. Poor timing by investors is often because of emotionally induced buying and selling.

Diversification

To reduce the emotional stress and poor timing decisions triggered by high stock market volatility, investors have traditionally diversified their portfolio into assets other than stocks. The main alternative has been bonds. But as we saw from the above charts, our long-run expected return decreases substantially as we add bonds or assets other to an all stock portfolio.

Yet this diminished return has not stopped investors from adopting so-called balanced portfolios, such as the common one that allocates 60% to stocks and 40% to bonds. Even Harry Markowitz, the father of modern portfolio theory, split his personal investments equally between stocks and bonds.

Based on financial planning principles, some investors start off with a higher allocation to stocks in their early years and then switch to a greater allocation to bonds as they grow older. This may no longer be as prudent a strategy as it once was. The average life expectancy today when someone reaches the age of 65 is an extra 19 years. This lengthening of retirement years and emphasis then on bond investing can aggravate the problem of portfolio under performance. Investors may need a way to keep growing their investment assets well beyond their retirement age.

Wealth Accumulation

What exactly does the old paradigm of a balanced stock and bond portfolio mean for wealth accumulation over the long-run? To determine this, I looked at a rolling 40-year window comparing the performance of the S&P 500 to a portfolio invested 60% in the S&P 500 and 40% in10-year U.S. Treasuries from 1900 through 2014.

During that time, the average annual total return of the S&P 500 was 10.0%, compared with 8.3% for the 60/40 stock/bond portfolio.[1] Applying these average rates of return to a 40-year investment horizon, a $10,000 initial investment in the S&P 500 would have grown to $537,000 before expenses and taxes, while a $10,000 investment in the 60/40 stock/bond portfolio would have become only $273,500. Due to the power of compounding, an all-stock portfolio would have resulted in almost twice the accumulated wealth of a 60/40 balanced portfolio.

Many do not realize the impact over time of an extra 1-2 % per year in return and what a large difference it can have on one's accumulated wealth. (There might be far less money under active management now if all investors were aware of this fact.)

High Costs of Diversification

Besides lower expected risk premiums, there are higher costs associated with diversification that many investors are not aware of. In their study “Fees Eat Diversification’s Lunch,” Jennings and Payne (2014) state that fees on diversifying assets are astonishingly high relative to their benefits. (On a real time basis, other assets have to compete with U.S. stock index funds having annual expense ratios of only 4 or 5 basis points.)

In the 1970s, U.S. investors started to look at diversifying their stock holdings internationally, despite the fact that non-U.S. stocks since 1900 have returned on average 2% less per year than U.S. stocks. Jennings and Payne found that fees reduced the benefit of international diversification by one-third for small institutional investors. Fees almost completely eliminated any diversification benefit from investing in emerging market bonds, hedge funds, and private equity. In looking at 45 different asset classes, Jennings and Payne found that fees consumed over half the expected benefit in more than 60% of those markets.

A Practical Solution

Is there anything investors can do to reduce their downside exposure during equity bear markets without giving up half their accumulated wealth in the process? Our dual momentum based Global Equities Momentum (GEM) method diversifies one’s portfolio in a more intelligent way.[2] GEM’s core holding is the S&P 500 in order to capture the highest long-run risk premium. GEM switches between U.S. and international stocks according to relative strength price momentum, which can improve the expected return from holding stocks. The GEM model also switches between stocks and bonds in accordance with trend-following absolute (time-series) momentum. When equities have been going up according to the rules of absolute momentum, GEM stays fully invested in stocks. When the trend of the stock market turns negative, GEM switches into low-duration aggregate bonds. Bear markets in stocks often foreshadow economic recessions with falling or flat interest rates. These are often the best times to hold bonds. There is also then a flow of funds from stocks to bonds by investors. Dual momentum is an adaptive approach that diversifies in a temporal way, which makes the most sense.

Here is the performance of GEM compared with the S&P 500 index and a portfolio allocated 60% to the S&P 500 and 40% to10-year Treasuries from January 1974 through June 2015. Positions are rebalanced monthly.

Performance of GEM

GEM S&P 500 60/40
Ann Rtn 17.73 12.33 10.76
Std Dev 12.36 15.43 9.74
Sharpe 0.89 0.41 0.50
Max DD -17.84 -50.95 -30.54

GEM has a lower worst drawdown than the 60/40 stock and bond portfolio. Besdies providing greater downside protection than afforded by the 60/40 portfolio, GEM returns have been substantially higher than the returns of the S&P 500 portfolio. Large losses in the S&P 500 need to be recouped before stocks can again show a net profit. For example, it takes a 100% gain to get back to even after a 50% loss. By sidestepping severe bear market losses, GEM can earn higher overall profits.

GEM remains in stocks when the trend of the stock market is positive to capture all it can of the high risk premium associated with stocks. GEM retreats to the safety of bonds during the 30% of the time when stocks are weak and bonds are often their strongest.

Possible Concerns

Why would anyone want to adopt a permanent stock/bond portfolio with its fixed income drag on performance when a simple dual momentum approach like GEM has shown less downside exposure and higher expected return than either an all-stock or a balanced stock/bond portfolio?

The first reason for some is that the future may not be like the past.But dual momentum is a simple model with several hundred years of out-of-sample performance to support it. The GEM look back parameter used by Cowles and Jones in 1937, has held up well back to the early 19th century and up to the present time. There are also good reasons, as described in my book, why the momentum effect should continue to persist.

The next concern may be occasional re-entry lags when a new bull market begins after dual momentum has protected your portfolio from the preceding bear market. There may also be occasionalwhipsaw tradesat other times that can cause dual momentum to lag behind the stock market. Over the past 40 years, GEM underperformed the stock market in 1979-80 and 2009-10. No strategy can outperform all the time.

Career risk associated with tracking error, long-standing aversion to market timing, and confirmation bias may keep institutional investors from ever using dual momentum.[3] As an encouraging note for the rest of us, this attitude should help keep momentum from being over exploited.

Since bonds make up 20% of GEM's profits, there may be some concern that bonds may not perform as well in the future as they have over the past. GEM uses aggregate bonds with around only a 5.3 year average duration, which gives them low sensitivity to interest rate changes. GEM uses bonds when there are bear markets in stocks. These often precede recessions which often lead to falling rather than rising interest rates.

Finally, the trend-following component of GEM is slow moving to minimize whipsaws. This means that GEM is still subject to the volatility associated with short-term stock market fluctuations. Conservative investors can always allocate a modest portion of their portfolio permanently to bonds so as to reduce this volatility.

Volatility Attenuated Dual Momentum

Here is what would have happened if we had allocated 75% of our investment portfolio to GEM and 25% permanently to aggregate bonds from January 1974 through June 2015.

	GEM	GEM/25	60/40
Annual Rtn	17.73	15.34	10.76
Std Dev	12.36	9.69	9.74
Sharpe	0.89	0.92	0.50
Max DD	-17.84	-11.88	-30.54

The GEM/25 allocation now has the same volatility as the 60/40 portfolio, but GEM/25 has a substantially higher annual return and Sharpe ratio. The maximum drawdown of GEM/25 is only 39% as large as the maximum drawdown of the 60/40 portfolio.

We see that dual momentum in various forms meets our fundamental goal of investing – the most gain with the least pain.

[1] Both portfolios had the same 40-year minimum average return of 5.4%. On the basis of avoiding the lowest average portfolio return, the 60/40 portfolio was not any better than the S&P 500 portfolio over a 40-year time frame.
[2] The GEM model is disclosed in my book, Dual Momentum Investing. It takes only 1 or 2 minutes per month to apply it.
[3] Even those who understand and appreciate momentum can be subject to long-standing biases that keep them from using momentum in a significant way.

↧

Perils of Data Mining

September 13, 2018, 11:33 am

≫ Next: Factor Investing: Buyers Beware

≪ Previous: Back to Fundamentals

From the time my book was published others have tried to improve upon the book’s Global Equity Momentum (GEM) model. There is nothing wrong with trying to improve on prior work. That is how society progresses.

But such attempts can have data mining, overfitting, and selection bias issues. Data mining is when you search through data to find more variables or better model parameters. These may not hold up going forward, especially if you have a limited amount of testing data.

Many develop quant models by data mining 15 to 20 years of ETF or mutual fund data which is as far back as that data will let you go. Here is a chart from my book showing how regimes change considerably every 15 years.

Searching for parameters based on only 15 or 20 years of data are likely to give disappointing results going forward as regimes change. Even 40 years of data may not be enough to inspire full confidence.

Model overfitting happens with having complex models. John von Neumann said, “With four parameters I can fit an elephant and with five I can make him wiggle his trunk.”

Selection bias is when you know what your testing results are likely to be ahead of time and build a model incorporating that information. You might select data or your data starting point knowing it will give good results while ignoring other possibilities.

Here is the most egregious example of selection bias that I know. An advisory firm invited me to dinner to discuss licensing my proprietary models. I thought this odd since they already had their own momentum-based models. At dinner I asked them why their published results only went back only 13 years when there was more data available. They said it was because investors do not like to see drawdowns greater than 20%!

Selection bias, model overfitting, and data mining issues may not be obvious or intentional. Here is what I have done to try to avoid these problems.

Use as much as data as possible.

Our first GEM backtest began from 1974. We were constrained by the amount of bond data that we had then. When I acquired more bond data, we extended our backtest to 1971 where we were now limited by the amount of MSCI non-U.S. stock index data. The extra 3 years of performance gave us an out-of-sample period covering the 1973-74 bear market. GEM performed well out-of-sample by being out of stocks during most of the bear market.

We recently gained access to non-MSCI stock index data and were able to extend our GEM backtest to Jan 1950. Asness, Israelov, and Liew (2017) in “International Diversification Works (Eventually)” also used 1950 as a starting date for their study. During World War II almost no one invested globally. The Templeton Growth Fund that began in 1954 was the first international fund available to U.S. investors .

Historical data and analysis should not be taken as an indication or guarantee of any future performance. Future performance of GEM may differ significantly from historical performance. Please see our Disclaimer page for additional disclosures.

While you should never just rely on the past performance, GEM did continue to outperform during the 1950s and 1960s.

Respect prior studies and well-established ideas

Researchers have studied momentum more than any factor in finance. Geczy and Samonov (2017) looked at momentum applied to geographically diversified stock indices, bonds, currencies, commodities, stock sectors, and U.S. stocks back to 1801. Momentum outperformed buy-and-hold in all these areas. The best results were with global stock indices shown below as “Equity”. These are what we use with momentum.

Source: Geczy & Samonov (2017), “Two Centuries of Multi-Asset Momentum (Equities, Bonds, Currencies, Commodites, Sectors, and Stocks”

We use a 12-month momentum lookback because Cowles & Jones found it worked well in 1937. Jegadeesh & Titman did also in their seminal momentum research done in the 1990s. Greyserman and Kaminski (2014) showed that trend following momentum with a 12-month lookback outperformed buy and hold back to the year 1223!

Keep things simple

We prefer to be holding stocks as much as we can since they have the most proven risk premium. We keep things simple by being in U.S. or non-U.S. stock indices according to their relative strength over the preceding 12 months. For non-U.S. stocks, we avoid selection bias by being in as broad an index as possible. That is the MSCI All Country World Index ex-U.S (ACWI ex-US). It includes all non-U.S. MSCI developed and emerging countries weighted by their market capitalization. When the trend in stocks is negative according to 12-month absolute momentum, we exit stocks for the safety of aggregate bonds. We have always tried to follow Einstein’s advice of keeping things as simple as possible but no simpler. Let us look instead now at variations of dual momentum appearing on the internet.

Shorter lookback periods

In my book I show that a 12-month look back period outperformed 3, 6, and 9-month look back periods with GEM. For years now, my website’s FAQ page has described in more detail why 12 months works best. Yet there are those who still believe that because shorter look backs are more sensitive to market changes, they should give better results.

A 3-month look back has performed well over the past 20 years. If you were to look only at that data, you might feel reassured about using a shorter look back period. But this starts to unravel in 1979-80 when the markets became very choppy. Choppiness gives both lower returns and higher drawdowns. Here are GEM results from Jan 1971 through Aug 2018 comparing 12 and 3-month look back periods.

	12 MONTHS	3 MONTHS
CAGR	16.6	13.6
ANNUAL STD DEV	12.2	11.8
SHARPE RATIO	0.94	0.74
WORST DRAWDOWN	-16.8	-23.3

Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

There are also tax advantages to a 12-month look back. Using it, GEM trades on average 1.3 times per year. Seventy percent of GEM’s gains are long-term, while 100% of its losses are short-term. Trading increases and these tax advantages disappear if you use a shorter look back. A 12-month look back worked well in the pioneering momentum research done in 1937 and 1993. Using a 12-month lookback reduces data mining concerns and seasonality bias.

EAFE instead of ACWI ex-U.S.

There are several websites that show GEM results and issue GEM signals using an ETF for the MSCI EAFE index rather than the broader MSCI ACWI ex-U.S. index. Emerging markets and Canada are missing from the MSCI EAFE index.They make up 24% of the MSCI ACWI ex-US index. Here are the GEM results using each index since the MSCI ACWI ex-U.S. index was introduced in December 1988.

	ACWI ex-US	EAFE
CAGR	15.6	14.3
ANNUAL STD DEV	13.1	13.0
SHARPE RATIO	0.84	0.73
WORST DRAWDOWN	-17.0	-17.0

Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

GEM earned 130 basis points more in annual return using the MSCI ACWI ex-U.S. rather than the MSCI EAFE index. I see no reason to not expect differences in future returns. I would thus stay away from using MSCI EAFE ETFs if possible.

U.S. small and mid-cap stocks

Some think using a total U.S. stock market index should do better than the S&P 500 index since broader indices include small and midcap stocks. We can easily check that out.

The broader U.S. stock indices have performed similarly to one another. I will use the Russell 3000 since it has the longest price history.

Here are GEM results comparing the S&P 500 to the Russell 3000 from when the Russell 3000 began trading in January 1979.

	S&P 500	Russell 3000
CAGR	18.7	17.06
ANNUAL STD DEV	13.9	13.9
SHARPE RATIO	0.99	0.86
WORST DRAWDOWN	-16.8	-23.3

Source: “Equity-Bond Correlation: A Historical Perspective”, Graham Capital Management Research Note, September 2017

Here are GEM results with the Barclays U.S. Aggregate bond index versus the Barclays 20 Year Treasury bond index from when both became available in January 1976.

	AGG Bonds	20 YR Treasuries
CAGR	17.5	18.1
ANNUAL STD DEV	12.5	13.6
SHARPE RATIO	0.98	0.94
WORST DRAWDOWN	-16.8	-17.0

Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

On a risk-adjusted basis, 20-year Treasuries did not outperform intermediate-term bonds despite low stock-bond correlations and a strong bull market in bonds. Under more normal market conditions, long term bonds with their higher risk are likely to be at a disadvantage as a safe asset. See here for more on long-term bonds as a crisis asset.

Other markets

Whenever a sector or factor fund is strong, I get emails asking if I have looked at adding it to GEM. To answer these questions, I used long-term index data to see if adding any of the following would have improved GEM results: small cap, value, low volatility, quality, stock momentum, equal weight, REITs, commodities, and the NASDAQ 100. None added value to GEM.

There are quantitative models that get better results by including larger than market cap allocations to emerging markets (EMs) in their backtests. EMs did particularly well in the late 1990s and mid 2000s when newly liberated EM countries had rapid export growth and large capital flows.

EMs show an improvement in GEM if you use only the MSCI EM data which begins in 1988. When I added pre-MSCI EM data to GEM, the results were disappointing. Drawdowns and volatility increased substantially. The same thing happened with sector rotation when I obtained an additional 20 years of sector data back to 1973. These examples again show the importance of longer data sets. The statistician Edwards Deming said, “In God we trust. All others bring data.”

Recent data mining example

I have gotten emails recently asking about a strategy called “Accelerating Dual Momentum” that was inspired by GEM. This model looks at 20 years of mutual fund data. Based on that data, the developer uses long-term Treasury bonds as his safe harbor asset and a combination of short look back periods. We have seen that these may not be the best choices based on longer term data.

The developer also questions using the MSCI ACWI ex-U.S. index of large and mid-cap stocks as the best vehicle for non-U.S. equities. His argument is that companies are more globalized now, so the correlation between U.S. and non-U.S. companies is higher than it once was. This may be true. But the following chart from my website’s FAQ page shows something else happening. The relative strength difference between U.S. and non-U.S. equities is due mostly to macro-economic conditions reflected in the strength or weakness of the U.S. dollar.

The developer’s solution to what he perceives as a correlation problem is to use a small to midcap international stock fund in place of a large cap international fund.

The starting date of the small to midcap MSCI ACWI ex-U.S. index is May 1994. There is not enough history there to make a good assessment of non-U.S. small to midcap performance. But we know that small cap international stocks do not show a statistically significant size premium as noted here.

An ACWI small to midcap ex-U.S. index fund began only in 2009. So in place of an index fund, the developer uses an actively managed small to midcap international fund. Looking at a large universe of funds, one can always after the fact find a few actively managed funds that have outperformed similar index funds. But there is a problem with that approach. Fund performance may be persistent over the short-run due to momentum. But over the long run, there is no meaningful relationship between past and future fund performance. See here for more on this.

At the end of his discussion, the developer presents a chart showing the rolling real return of GEM versus his model. Even after all his data mining efforts, the real return of both models over the past 30 years has been about the same. If the developer had calculated GEM correctly, GEM would have been the winner.

What is surprising

It is not surprising that people try to develop or modify models using 20 years of data. Most do not realize how much uncertainty exists when you use this amount of data. What is surprising though is how many people ignore contrary evidence. Much of the information I presented here is in my book. All the information and more are on my website’s FAQ page. But people still cling to their prior beliefs and ignore contrary evidence.

Perhaps this should not surprise me. In the 1960s and several times since then, academics have shown that actively managed funds underperform index funds. Yet fifty years later, only 35% of total U.S. fund assets are in index strategies.

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