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World's First Index Fund

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Index funds are pervasive now.  We wouldn’t have our momentum program without them.  But did you ever wonder how and when the very first index fund started? Many think it was in 1971 when Wells Fargo (which became Barclays Global Investors before being acquired by Black Rock in 2009) put $6 into every stock on the NYSE for the Samsonite pension fund. But that’s not correct. Let me tell you how it really began.

In 1976 I was with Smith Barney & Co. At that time Smith Barney was a prestigious investment banking and institutional brokerage firm along the lines of Morgan Stanley or First Boston. However, the firm wanted a retail distribution network, so they acquired Harris Upham, a predominantly retail oriented firm. As is usual after these kinds of acquisitions, Smith Barney let go of the redundant operations of Harris Upham.  However, Harris Upham had one of the very best Over the Counter (OTC) market makers in the business, so Smith Barney was happy to keep him. I’ve long forgotten his name, so let’s just call him John.

In those days, there was no electronic marketplace. In order to buy or sell OTC securities, you had to phone around to different brokerage firms and check the bid/offer spreads maintained by their market makers. A top notch OTC guy could become a terrific brokerage firm profit center. This was not just because the bid/offer spreads of OTC securities were sometimes large enough to drive a small vehicle through. The best OTC market makers made plenty of bucks from their acumen in maintaining inventories of OTC stocks. They could slant their bids and offers so they ended up with large positions in stocks they liked and small positions in those they disliked. John was one of the best stock pickers in the business. Top institutional investors would deal with him in order to find out what he liked or disliked, and to execute their trades using the large, liquid inventories that John routinely maintained.

Smith Barney was proud to have John on board. They sent him around to all their offices so we could learn more about John and feel comfortable sending business his way. One day John showed up at our office and spent an hour explaining what he did.  He impressed all of us with his expertise and accomplishments. When he finished his talk, we congratulated and complimented him.  As we were about to adjourn for lunch, someone said how much he admired John’s trading abilities. John sat back in his chair, paused a moment, then remarked, “Yes, I’ve done very well, but would you like to hear about someone who’s done even better… in fact, someone who is the best investor that I know?”

Everyone froze in their tracks. “Sure!” we said as we all settled back into our chairs. John continued, “The greatest investor I know, the one who has outperformed every professional portfolio manager that I’m aware of, is … my wife, Mary. Would you like to hear how she does it?”

You could have heard a pin drop. During the stunned silence, if an eight hundred pound gorilla had entered the room, no one would have noticed. Here was one of the industry’s top traders and market makers, who did business with the world’s best money managers, telling us that his wife was better than all of them. And he was about to tell us how she did it!  

John continued, “Mary has always been very patriotic. So years ago she decided to buy all the stocks with U.S. or American in their names. So she bought U.S. Steel, U.S. Shoe, U.S. Gypsum, U.S. National Bankshares… American Brands, American Can, American Cyanamid, American Electric Power, American Express, American  Greetings, American Home Products, American Hospital Supply, American International Group, American Locomotive, American Motors, American South African, American Telephone & Telegraph, British American Tobacco, North American Aviation, Pan American Airlines, and many smaller companies.”

We didn’t know if John was serious or putting us on. But John wasn’t smiling. He seemed sincere and continued, “After a number of years, Mary did so well that she decided to buy all the Generals – General Dynamics, General Electric, General Mills, General Motors, General Maritime, General Steel, General Telephone, Mercury General, Media General, Portland General, etc…  Mary’s done better with her portfolio than anyone I know, and that’s the God honest truth.”     

Everyone was highly amused as we adjourned for lunch. But I couldn’t stop thinking about Mary. I kept asking myself how she could have outperformed the world’s top money managers over such a long period of time. Then it became clear to me. First, Mary’s portfolio had relatively little in the way of transaction costs. She bought stocks only once and held on to them forever. Commissions were much higher back then, but that wasn’t the whole story.  Mary also didn’t pay any management fees. That saved her at least 1% a year compared to mutual funds and other actively managed investment programs. And finally, Mary’s portfolio was well diversified. Most actively managed portfolios then were biased toward a particular investment style, such as growth, glamour, large cap, etc. Deviations from the market portfolio create risk. Mary’s portfolio was like the entire market - equally balanced among small cap, large cap, value, growth and every other factor. If anything, her randomly selected portfolio had a value and small cap tilt, compared to the general market. Mary had created the world’s first index fund, without the need of brokers, money managers, or anything else… other than a dictionary. After thinking deeply about what Mary had done, I decided to leave the brokerage business.   

Efficient Markets....Not

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In my last post I mentioned how I quit the brokerage business after thinking about The World’s First Index Fund, which had outperformed most of the world’s top investment managers. In fact, after ruminating about this, I applied to and was accepted into the finance PhD program at the University of Chicago, as well as the PhD program at Wharton. These were traditional bastions of the efficient market hypothesis (EMH), which postulates that security prices reflect all publicly available information, and the market cannot be beat.

I appreciated many of the underpinnings of modern finance, such the importance of cost minimization, the risk reduction benefits of diversification, and the difficulty of outperforming Mr. Market.  But I was forced to change my mind about a career in academic finance for several reasons. First, I had read the Nick Darvas and Bob Levy books about relative strength/momentum investing. I was also familiar with a number of well-respected value managers, such as John Neff, William Ruane, Walter Schloss, and Max Heine, all of whom had consistently outperformed the market. I personally knew hedge fund managers who did very well by focusing on unexploited market inefficiencies. I couldn’t believe all this was due to luck or chance. I also had trouble accepting that risk and return should be quantified and expressed in terms of an asset's relationship to the US stock market.  Single factor CAPM was king back then.

So I passed on the PhD programs, figuring I would be tarred and feathered or run out of town as a heretic before I could earn my degree. Andrew Lo, an MIT economist, tells a humorous story about doing research on technical analysis some years ago and getting good results. Demonstrating extreme confirmation bias, one of Lo’s colleagues examined his work and commented, “Your data must be wrong…”  EMH had become a belief system akin to religion.   
                                          
Instead, I went to Harvard for an MBA degree and started exploiting market inefficiencies as a private money manager. Imagine my surprise in 1992, when Fama and French, the fathers of EMH, published a paper showing that size and value matter. The market couldn’t account for the excess returns from those factors.  F&F tried to make these anomalies seem consistent with their belief in efficient markets. To me, their logic was not very convincing.

In 1993, the EMH was challenged more seriously by Jegadeesh & Titman in a paper that firmly established momentum as the premier market anomaly. Momentum is at least twice as powerful the value anomaly. (See Asness et al, "Value and Momentum Everywhere" where the Exhibits show an alpha of 1.5 and 3.4 for long-only global stock value and momentum, respectively).  Hundreds of research papers since then have validated momentum as a market beating approach that works across nearly all markets and time periods.

Yale economist Robert Schiller describes the EMH as “…one of the most remarkable errors in the history of economic thought.”  My Optimal Momentumresearch paper offers some of the strongest evidence to date that the market can be beaten. I used a simple six month momentum model, featured in many research papers, to earn twice the return of the stock market with half the downside volatility over the last 34 years. The EMH says extraordinary returns like these shouldn’t exist because investors, acting rationally, should push those returns toward equilibrium until any extraordinary profits disappear.  If this doesn't happen (and it hasn't yet), it means investors don’t always behave rationally. Rational behavior is an underlying assumption of efficient markets.

Another example of the failure of efficient market thinking is the ill fated experiment with “portfolio insurance.”  This was a concept developed by finance professors who said one should increase long exposure when the markets move up quickly and decrease long exposure when the market drops quickly. This was supposed to create the effect of derivatives hedging. However, anyone with much practical experience knows this is a bad idea, since markets are short term mean reverting. Excess emotion or other issues often cause markets to overreact then reverse. Stock exchange specialists and floor traders have always made a good living fading short term extreme market moves. Portfolio insurers packed up their bags following the sharp market down move and rebound in late 1987 that gave them large whipsaw loses.

Persistent extraordinary returns and market mean reversion aren’t the only contraindications to the EMH. Calendar effects, government-backed mortgage securities premia, large differences between historic and implied volatilities, and other factors also point toward market inefficiencies.

Investors preferring lower risk adjusted returns in the face of overwhelming evidence that they can do better is another sign of investor irrationality, and hence market inefficiency. This is what has happened since the advent of index funds.  Only one-third of actively managed mutual funds outperform index funds each year.  Only a handful beat index funds over longer periods of time, and it’s impossible to identify in advance which those will be. Because of higher management fees, transaction cost, and tax inefficiencies, actively managed mutual funds, as well as actively managed 401k and other retirement plans, typically earn 2.5% to 3% per year less than index funds. No wonder Warren Buffett has said, “Most investors, both institutional and individual, will find that the best way to own common stocks is through an index fund that charges minimal fees.” Yet, according to the Investment Company Institute, index funds make up only 13% of the assets managed by registered investment companies, including those that manage ETFs.

I don’t intend this to be a criticism of investors who want to take a shot at Mr. Market by choosing an interesting asset allocation fund like Permanent Portfolio or a decent alternatives fund like TFS Market Neutral or the Arbitrage Fund. I’m talking instead about the vast majority of investors who choose plain vanilla, actively managed mutual funds rather than better performing index funds. If you compare the extraordinary returns from cross-asset momentum investing with the sub-market returns of most actively managed mutual funds, it’s even harder to believe that investors are rational and that markets are truly efficient.

Momentum Express

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Momentum is like being on an express train headed toward the riches of Golconda. On our journey, if the Equity Express we’re on is passed by a faster moving train, we hop on over to the new train in order to keep moving forward as quickly as possible. Occasionally, all the Equity trains come to a stop. Some may even go into reverse. When that happens, we may jump on the powerful Gold Express if it’s headed in the right direction. If not, we can always transfer to the Tortoise Freight, powered by the returns earned on short term treasury notes. It consistently moves forward, albeit slowly and steadily. Once the Equity Express gets moving along nicely again, we settle back on it and enjoy the rewarding ride.

On our pleasant journey we speed past the Lake Wobegon loco motive, actively engineered by a small army of mutual fund managers who all think they are superior to one another. We rocket by the Buy and Behold, with its efficient conductor who randomly walks about shouting, “…damn the torpedoes, full speed ahead!” He’s forgotten he has to traverse the ups and downs of great hills and valleys.

Next we pass the alternative train yard full of expensive hedge fund engine wrecks and long/short conveyances going around and around in silly circles but getting nowhere. Finally comes Dante Station, with its flashing neon sign, “Abandon Hope All Ye Who Enter Here.” Led Zeppelin’s “Dazed and Confused” loudly blares from the station loudspeakers. We wave to all the poor souls just sitting there on their assets doing nothing.

All aboard the Momentum Express!

Economics... the dismal whatever

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How many sciences do you know that have the word voodoo associated with them? How many have had major paradigm shifts every 20 years?

Economics is still categorized into various schools of thought. When was the last time you heard of schools of chemical thought or schools of biological thought?

Joseph Stiglitz, a Nobel prize winner in economics, has said that economics is being taught incorrectly in most of the world's universities. Brad DeLong, a respected economics professor from Berkeley, recently said, "I used to--six years ago--be certain that ... economics had a powerful technocratic core and a powerful set of analytical tools that helped to make sense of the world. But the treatment that the world has gotten from the Lucases, Cochranes, Famas, Kocherlakotas, and many others, not to mention the Prescotts--none of whom seems to have made any effort to mark their prejudices to reality--has shaken my confidence to the core. They seemed to me and seem to me to have simply not done their homework, and not be trying to do their homework."

So whenever I think of economists, I remember Mark Twain's words, "It ain't what you don't know that gets you in trouble - it's what you know for sure that just ain't true." And as Joan Robinson says, "The purpose of studying economics is not to acquire a set of ready made answers to economic questions, but to learn to avoid being deceived by economists."

Here is a good summary of the current state of economic thought:    Neo -Voodoo Economics.

Key Momentum Factors

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There are three key factors in structuring a momentum program. They are all known through momentum research, so it's not hard to get them right. 

The first key factor is the formation look back period, i.e., how many months back do you measure relative strength momentum.  In 1937, Cowles and Herbert came up with 12 months. In 1967, Levy used 6 months. In 1993, Jegadeesh & Titman showed that momentum works well using anywhere from 3 to 12 months, with the best results being 6 to 12 months.  Literally hundreds of research papers since then have reconfirmed the optimal 6 to 12 month time window. It has held up consistently when applied to data from the 1890s until now.  It works with nearly all markets and asset classes.

There is a sharp drop off in momentum profitability once you extend beyond a 12-month formation period. As you reach 3 to 5 years of past data, mean reversion becomes strong, giving you the opposite effect of momentum. 

Anyone needing convincing of the optimal 6 to 12 month formation window can look at the key momentum research papers on AQR's Annotated Bibliography. You can also go to the Social Science Research Network website and do a search on the word "momentum."   You will find dozens of papers that all successfully use a 6 to 12 month time frame.
  
The second key momentum factor is the chosen universe of available investment opportunities. Currently, most investment programs that use momentum apply it to either individual stocks or, occasionally,  industry groups. But as you know from my research paper, momentum is more powerful when it is applied across a group of non-correlated assets.  The paper, Momentum and Value Everywhere, also illustrates this point.

It's also desirable for one's momentum portfolio to include a way to retreat to the safety and stability of cash or short term fixed income securities.  By not including these in your momentum portfolio, you forgo the risk reduction that comes from adapting to market conditions by opting out of risky assets early during market regime changes. Absolute, or time series, momentum deals with this issue explicitly. It requires a positive trend as a prerequisite to momentum investing.This idea has not caught on yet, but it's time should come in the not-too-distant future.

The third and final key momentum factor is how many assets from your investment universe that you decide to use. We all know that diversification is a good thing. But more is not always better with momentum. Profits from long positions decline as you go down the ladder of assets ranked by momentum. 

                              Volatility
                                                                             Number of Assets


There is a dramatic fall off in volatility as one goes from 1 to 2 asset classes, then a lower drop in volatility when going from 2 to 3 assets, and so on. See the chart above for an illustration of how risk declines more slowly as you add asset classes. Momentum profits, on the other hand, drop in a more linear fashion as you add additional assets. The following example shows what happens if you use too many asset classes.

The SGI Global Momentum index universe consists of ten exchange traded funds in diversified assets including developed market equities (EU, US, Japan), emerging equities (Asia/Pacific, Russia, Latin America), listed private equity, bonds (Euro zone Government Bonds and Inflation-linked) and commodities. Equities funds make up 7 out of the 10 assets. The five best performing shares at any one time make up the index. The SGI index began in late 2007. Over the past 3 years, it has outperformed the MSCI World index by a little less than 2%, while having a maximum drawdown of 46%, versus 53% for MSCI World. Too many assets not only lowered the return of the SGI Index; it also did little to reduce the maximum drawdown of the momentum portfolio. With 5 assets in play at all times, there is nowhere safe to fully retreat to under adverse market conditions.    
  

Here Comes Market Neutral Momentum...sort of

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QuantShares recently launched the U.S. Market Neutral Momentum Fund (MOM).  This fund comes close to replicating the strategy followed in many momentum research papers.  From among a universe of the 1000 largest U.S. companies, the fund goes long the 200 strongest and short the 200 weakest companies based on 12 month past performance with a 1 month lag.  Positions are readjusted monthly and are filtered so as to be neutral with respect to sector weightings. The fund’s expense ratio (not including the extra costs of carrying short positions) is .81%. 

I was surprised to see this ETF, since long/short momentum using individual stocks has been subject to drawdowns of over 80% in the past. Even more surprising is the fact that QuantShares also has an anti-momentum ETF (NOMOM) that reverses the logic of their momentum fund. This must be for those who want a proven way to lose money. The only time this strategy has made sense in the past has been once every ten or twenty years following a massive market sell off, when anti-momentum ruled for a short time.  I’m still scratching my head about all this. I guess life is like a box of chocolates.  You never know what you’re gonna get.  I'd be very surprised if the QuantShares momentum funds are successful.

Is Momentum Really Dead?

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There have been some working papers out that purport to show the demise of momentum. Financial blogs have picked up on this information recently and passed it along to a wider audience. But as we shall soon see, momentum may just be going through a Mark Twain moment in which rumors of its death are greatly exaggerated.  Let’s take a closer look and see what’s really going on. 

“Are Momentum Strategies Still Profitable Work for U.S. Equity?”  came out in October 2010.  It showed that the mean monthly return from momentum during the five years ending in 2009 was -.163%. In contrast to this, the mean monthly momentum return from 1965 through 1998 was 1.14%, by the author’s calculations. Momentum returns were determined by going long the strongest and short the weakest 10% of stocks during the preceding six months. Positions were held for six months then readjusted. However, there are no statistical criteria, such as t statistics or p values, which might indicate any significance to these results. Nor are there any alternative momentum calculations, such as the use of different holding or formation periods. Finally, there is no exploration as to why momentum results may have changed over time.  So there isn’t much to go on here.

The second paper that challenges momentum came out in April of this year. It  is called “Momentum Crashes”.  In it, the author looks at momentum based on a twelve month formation period lagged one month.  The holding period is one month before portfolio reformation. Top and bottom deciles are again used to sort stocks into momentum longs and shorts.  Here the author identifies what has been going on under the surface. He shows there have been two major “momentum crashes” that have depressed momentum returns. The first occurred in July-August of 1932.  The second was in Mar-May 2009.

We see from this chart that if your analysis of momentum performance is for a short period of time (say the last five years that ends in 2009), results look lackluster. A single, rare event can seriously distort performance over a short evaluation period.  Over a longer time frame, however, momentum still looks good. Furthermore, momentum returns are depressed during these two “crash” periods only because momentum is looked at from both a long and short point of view.  The “crash” loses occurred on the short side during strong market rebounds following market declines with high volatility. During the 1932 “crash” period, past momentum losers rose by 236%, causing heavy losses as shorts, while past winners were up only 30%.  Similarly, during the 2009 crash, past losers were up 156%, while past winners were up only 6.5%.  However, most actual momentum investing is done only on the long side. If we look at how long only momentum did during these “crash” periods, we get an entirely different story.

Here are the monthly returns from the above winner minus loser (WML) momentum portfolio versus the AQR long only momentum equity index (top one-third stocks based on 12 month momentum lagged one month) during the worst WML momentum months of 2009:
                                                                                          WML                  AQR
Aug 09             -24.8                    2.3
Apr 09              -46.0                    1.8
Mar 09             -39.6                     5.3

Long only momentum looks good here.  For a longer term view, the folks at Dorsey Wright recently reported in a blog post called  Momentum Over Multiple Cycles  some long only momentum results using data off the Kenneth French website from 1930 through October 2011. Portfolios were selected based on being in the top half in market capitalization and the top third in momentum, which is similar to the AQR index methodology. Tracking ten year rolling returns from 1940 onwards, they discovered that the average ten year return of the momentum portfolios was 405%, versus 216% for the S&P 500 index. Momentum earned nearly double the rate of return of the market. In every ten year period, including the most recent one, the momentum portfolio outperformed the market portfolio.

But long only momentum has downside in the form of high drawdowns during equity bear markets. For example, the AQR equity index was down 49% from October 2007 through February 2009.  However, as those who have read my research paper know, momentum applied to only equities is not the best way to proceed. During this same time period, our momentum portfolio using multiple asset classes achieved a profit of 18%. Momentum is still very much alive and well. Long live momentum!

Residual Momentum

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Often when a working paper is published, the free versions of it are no longer available. Residual Momentum by David Blitz, Joop Huij and Martin Martens never made it on to the SSRN site. It was published last June in the Journal of Empirical Finance and is still available in draft form if you click on the link above.

It's an interesting paper in that they determine momentum by the relative strength of the residuals after regressing stocks on market risk factors. They throw out beta and alpha and look at the residuals, which represent each stock's idiosyncratic (non-market related) risk. In comparison to total return momentum, residual momentum earns the same return with only about half the risk. According to the authors, residual momentum is also more consistent across different economic environments and has much lower drawdowns than total return momentum. The authors show that from 2000 through 2009, total return momentum had an average loss of 8.5% per year, while residual momentum gained an average of 4.5% per year.

I should point out that total return stock momentum has been stronger since 12/09. Its average annual loss on the long side since 2000 is now around 0.5%. There are also better ways to use momentum than with individual stocks, as my momentum papers demonstrate.

Value and Momentum...Not Here

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I like Cliff Asness. Although I'm not a fan of momentum applied to individual securities, Asness' firm, AQR Research, created the first fully disclosed momentum funds available to the general public (AMOMX,ASMOX,AIMOX). AQR's low volatility risk parity fund (AQRNX) has outperformed the other publicly available risk parity funds during the first  two years of its existence. Asness even has a sense of humor, saying he has recurring nightmares about being hacked to death by a pack of rabid black swans. More money managers should have such nightmares.

In 2009, Asness and his colleagues released a working paper called "Value and Momentum Everywhere". The paper did a nice job showing that momentum works well with many different asset classes - foreign and U.S. stocks, country bonds and indices, commodities and currencies. Asness et al also tried to show how value would have performed with these same assets. Identifying value with equities is pretty straight forward; book-to-market is commonly used. However, trying find comparable value metrics for other assets is not so easily done. There are no established ways for doing this, and efforts to do so may seem rather contrived. Nevertheless, Asness et al reach the conclusion that value and momentum do well when mixed together 50/50, since the correlations between them are weak.

The problem with that conclusion (besides the non-trivial one of determining value in non-equity markets) stems from the Asness momentum results.They are not as good as the ones we get from multi-asset momentum using asset classes rather then individual equities and a combination of relative and absolute momentum.

Since determining non-equitiy value can be problematic, let's look at just equities to see what I mean. Below are the returns from January 1975 through December 2011 for the MSCI U.S. Value and MSCI EAFE Value indices, as well as a dual momentum strategy that is long either U.S. Treasury bills, the MSCI U.S. index, or the MSCI EAFE index based on 12 month momentum. Positions are adjusted monthly. Transaction costs for momentum switches are negligible, with just 1.4 switches per year.


Dual Momentum
U.S. Value
EAFE Value
Annual Return
14.3
13.1
14.1
Annual Std Dev
12.4
15.1
17.7
Annual Sharpe
.66
.47
.45
Max Drawdown
-26.0
-54.6
-58.6


You can see that dual momentum easily approach outperforms the value indices. Let's now combine momentum with value as per Asness by weighting momentum 50% and each of the value indices 25%. Which results would you choose?


Dual Momentum
Momentum w/ Value 50/50
Annual Return
14.3
13.6
Annual Std Dev
12.4
12.3
Annual Sharpe
.66
.61
Max Drawdown
-26.0
-37.8


The superiority of momentum over value is greater when you construct multi-asset dual momentum portfolios, like we do, instead of using a portfolio that is limited to individual equities. Individual stock momentum may complement individual value stocks. But should there be value and momentum everywhere? I don't think so - not here anyway.


Momentum Crashes Revisited

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Last December I did a blog post called "Is Momentum Really Dead?" in response to several research papers that purported to show that momentum had lost its edge. Those papers were written due to two unusual occurrences in which long/short stock momentum suffered large loses. The first time this happened was in July/Aug 1932. The second occurrence was in Mar/May 2009. The losses occurred on the short side during sharp market rebounds following periods of extreme, once-in-a-generation, market weakness. I showed that long only momentum and multi-asset momentum never experienced this crash.  

It's a mystery to me why research continues to focus on long/short stock momentum. Long only momentum across asset or risk classes is much better than long/short momentum using individual stocks. However, the academic world has not generally woken up to this fact.

There is a recent paper called Managing the Risk of Momentum by Barroso and Santa-Clara that addresses the long/short momentum crash by adjusting position size according to volatility. This eliminates the crash, substantially reduces drawdown, and gives a boost to the Sharpe ratio.Volatility based position sizing is similar to risk parity and is related to mean variance portfolio sizing.

There are, however, some potential drawbacks to volatility based position sizing in a multi-asset context. First, volatility based portfolios are often heavily concentrated in fixed income. This creates a new risk factor not accounted for in portfolio modeling. With interest rates at such low levels now, the risk of a bear market in bonds is greater than the probability of continued bull market appreciation. On the positive side, interest rates don't often mean revert. They instead tend to drift. There may still be some profits if long bond rates drop further. But from a longer term perspective, the last time the yield on 10-year notes was as low as it is now, we entered into a bond bear market from 1935-1980.

The second reason to be cautious of volatility-based position sizing is that lower returns often accompany lower volatility. Although bonds have been a strong performer during the past 15 years, a portfolio heavy in fixed income securities has lower long-run expected returns than a portfolio that is more oriented toward higher risk premia assets, like equities. One can leverage a lower return fixed income oriented portfolio to boost returns, but some investors have constraints against the use of leverage. Leverage also increases a portfolio's interest rate, left tail, and correlation convergence risks. Increased leverage may be fine for hedge funds, but it has its drawbacks with respect to other accounts. 

































2012 NAAIM Wagner Awards

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The 2012 NAAIM Wagner Awards for Advancements in Active Investment Management were announced yesterday by National Association of Active Investment Managers.  I'm in Atlanta now to accept the first place award and $10000 prize, and to present my  paper at their annual conference. I'm honored to be receiving this recognition for my research work.    
 
During the past 20 years there have been some 300 published research papers written about momentum. Most have dealt with momentum applied to individual stocks, bonds, commodities, or currencies. (All four publicly available, fully disclosed momentum funds apply momentum only to individual stocks or commodities.) Less than five percent of momentum research has looked at asset groups. Fewer still have applied momentum simultaneously to multiple asset or risk classes, even though this gives the best results.

My research paper last year (the second place winner of the 2011 NAAIM Wagner Award) used fixed income as a safe haven from riskier markets under appropriate market conditions. This was an ad hoc step in the right direction. What was really needed was a logical framework that systematically deals with regime change in order to better exploit momentum's profit-making potential. 

In my new paper, I believe I've accomplished this. I show that volatility is the primary success factor that leads to momentum profits. I then show how to harness that volatility from within a momentum structure that adapts to regime change. You can download from NAAIM  my paper and all the other papers submitted for the Wagner awards, or you can download an updated version of my paper here. I welcome feedback and comments. My forthcoming book will further explore multi-asset, multi-risk factor momentum.

  

Currencies, Emerging Markets, & Commodities

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I am asked frequently why I do not include additional asset classes. This question probably stems from the popular, but erroneous, belief that more is always better. Some also believe that being more inclusive may reduce data snooping issues. However, there is method to my madness, and logical reasons why I exclude several commonly-used asset classes. 

First, as my latest research paper points out, high volatility is a success factor for momentum investing. Even though currencies, like most every other asset, benefit from momentum, their low volatility is one reason we exclude them from our portfolio. As cross rates, currencies also have no inherent risk premium. This contributes to their having low expected momentum returns. In Asness et al, "Value and Momentum Everywhere", currency returns and alphas were among the lowest of the assets they looked at.

An asset class that does have the requisite volatility is emerging market equity. Many investors feel that emerging markets merit being a separate asset class. However, there are unknown risks associated with these thin and illiquid markets. First, they have only about twenty years of price history. No one knows how emerging markets would have performed in October 1987, for example, but it likely would not have been a pretty sight. Because they can suffer from sharp and rapid price declines, one often aggregates emerging markets into baskets that trade as a group. This stems from the belief that diversification among emerging markets will reduce their risk. However, baskets of emerging market stocks add contagion risk, causing them to trade together as a whole. Aggregation and contagion can amplify and accentuate liquidity and systemic risks. During the Russian debt crisis, markets as far away as Singapore suffered major outflows of capital and extreme price volatility.

Not only are correlations higher now among the emerging markets themselves, but they are also higher between emerging and developed markets. The following chart shows how correlations between emerging and EAFE markets have risen substantially over the past fifteen years. (The correlations between EAFE and US markets have also risen substantially, but that's another story.) The five-year rolling monthly correlations were below .30 in the 1990's. For the past three years, the correlations have remained steady at over .90. From a diversification point of view, emerging markets have lost much of their appeal.


Another volatile asset class that has attracted a large following in recent years is commodity futures. The logic here is that commodities act as an inflation hedge. Yet real estate, natural resource, other high tangible book value stocks, and even Treasury bills can serve that same purpose. The underlying problem with commodity futures is that they, like currencies, are not an asset class in the normal sense. Stocks and bonds exist as vehicles for raising capital. In return for this, investors can expect streams of payments from bonds or residual cash flow from equities. 

Commodity futures, on the other hand, are a zero sum game in which the profits and losses of contract buyers and sellers are equal, disregarding transaction costs. There is no expectation of aggregate positive returns. Futures contracts cease to exist on their expiration dates, and there is no wealth created in these transactions. Because gains and losses are symmetrical to the buyer and seller of a futures contract, one cannot say that the buyer, by taking on volatility, is entitled to a positive return, since the seller, by the same reasoning, would also be entitled to a fair return. One of them must lose money for the other to gain the same.

In the past, buyers of commodity futures did often enjoy a systematic positive return called the "roll yield" that flowed from hedgers to speculators. Hedgers were generally short sellers who felt a need to lay off risks of the unknown in their capital-intensive business. Speculators, who had no need to participate in commodity markets, were induced to take the other sides of these trades because of the roll premium they received.

However, all this has changed during the past 15 years. Using data through the 1990s that showed commodities to be a decent portfolio diversifier, academic papers, like the one by Gorton et al in 2004, induced institutional investors to invest heavily in portfolios of passive commodity futures. Since then, endowments, pensions, hedge funds, risk parity programs, and the public have all scrambled to add over $300 billion of long commodity index futures to their portfolios.

Many pension programs now feel they should have 5-10% of their portfolio assets committed to commodities. This new group of speculators insists on going long regardless of price. As this group has become increasingly large compared to the number of hedgers, the roll yield dissipated and became negative. From 1969 to 1992, the roll return averaged 11% per year. Since 2001, it has averaged -6.6%. The odds are therefore stacked heavily against investors who passively hold long commodity futures.

Several commodity indices, like the PowerShares DB Commodity Index or the Summerhaven United States Commodity Index, have tried to reduce the roll yield disadvantage by selectively seeking futures contracts, when possible, that still offer a positive roll premium.

However, all commodity index funds, regardless of their roll premium capture inclinations, face another formidable obstacle. These are the front-running costs that come from regularly rolling over their positions. They occur when others anticipate and trade in front of the commodity futures rollover dates, then take profits afterwards. Zyiquan Mou of Columbia University estimates front-running costs at 3.6% annually from January 2000 through March 2010. JP Morgan Commodity Research reorted in 2009 that roll returns have put a drag of 3-4% per year on commodity index returns since 1991. These hidden costs can quickly take the wind out of the sails of the commodities futures indices.

Rising correlations are another problem associated with commodities.  The average correlation coefficient between equities and commodities, which was -.27 from 1970 through 2003, has risen into positive territory over the past five years. More importantly, during both the 1929 stock market crash and the 2008 financial crisis, the correlation between equities and commodities shot up to over 80%. Commodities diversification was lacking when it was needed the most. 
  
A 2011 research paper by Daskalaki & Skiadopoulos called "Should Investors Include Commodities in Their Portfolios After All? New Evidence," shows that the introduction of commodity instruments in a traditional stock/bond portfolio is no longer beneficial for a utility maximizing investor. This is based solely on past performance and not the additional reasons given above. From January 1975 through December 2011, the GSCI had an annual average return of 6.1% and standard deviation of 19.3%, versus a 7.7% return and 4.3% standard deviation for five year Treasury bonds. We can see that the evidence is strong from several points of view now that passive commodity indices are no longer such a good addition to traditional stock/bond portfolios.

New version of my paper is now available...

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I just made over a dozen minor changes and additions to my latest paper, "Risk Premia Harvesting Through Momentum". You can download it by clicking on the link.

Einstein said,  "Everything should be made as simple as possible, but not simpler." My book will present a somewhat simpler and more direct version of my paper's momentum methodology. Yet it is actually stronger and more robust. I may include a few excerpts here in the months ahead.

Introducing the Balanced Momentum Index

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I am happy to introduce our multi-asset, rules-based Balanced Momentum Index (BMI) that incorporates both relative and absolute momentum. I was expecting to release a global momentum index. However, after careful consideration, I decided to first release a U.S. based index. The reason has to do with the steadily rising correlations between U.S. and foreign markets over the past 15 years, as you can see here:


Given the interconnectedness of world capital markets and globalization of the world's economies, for the past three years this correlation has remained steady at over .90. Furthermore, when markets drop, correlations typically go up even more, which is when it is least desirable for this to happen

Here are the comparison performance statistics from January 1984 through May 2012 for the BMI compared to a benchmark portfolio of 60% Russell 1000 index and 40% Barclays Capital U.S. Aggregate Bond index:


   BMIBalanced 60/40
Annual Return  14.06       10.13
Standard Deviation    8.18         9.82
Max Drawdown-12.85     -32.29
Sharpe Ratio    1.13         0.58

Maximum drawdown is on a month-end basis. BMI uses relative and absolute momentum applied to the following indices: Russell 1000, NAREIT Equity REIT, Barclays Capital U.S. Treasury 20+ Year, Bank of America Merrill Lynch U.S. Cash Pay High Yield, and Bank of America Merrill Lynch 3 Month U.S. Treasury Bill.  January 1984 was chosen as the BMI start date because the High Yield bond index began that year. Monthly BMI performance updates will be posted on our website Research page around the fifth business day of every month.




Historical data and analysis should not be taken as an indication or guarantee of any future performance.
See the website research page for additional notes.
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Time Series Momentum

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Moskowitz, Ooi, and Pedersen recently posted a paper on SSRN called "Time SeriesMomentum." This same paper was published in last month's Journal of Financial Economics. Most momentum papers deal with cross-sectional momentum, in which a security's out performance relative to its peers predicts future relative out performance. In time series momentum, a security's own past excess return predicts its future performance. This is functionally equivalent to "absolute momentum," that I describe in my paper.

The authors examine time series momentum across equity indices, currencies, commodities, and bond futures. They find that a diversified portfolio using 12-month time series momentum with monthly rebalancing earns substantial abnormal returns and performs best during market extremes.

It is good to see validation of the absolute momentum concept. The best scenario, however, is a combination of both absolute and relative momentum, as per my research paper. That way you can potentially profit from both the relative strength benefits with respect to asset selection, as well as the trend following, market timing benefits inherent in absolute momentum.

How to Judge an Investment Opportunity

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Whenever a new investment approach comes along, there are always risks associated with data snooping and selection bias. Even with the best thought out methods using an abundance of past data, there is still some data mining involved in selecting portfolio assets, weighting methods, rebalancing intervals, etc. Just as one can never become a virgin again, so one can never unlearn all the ideas that may become embedded in an investment methodology.

So how can one minimize the risks associated with a new investment approach? The first way is to require that the method make sense. Is it in tune with the nature of the markets?

Portfolio insurance, an idea promoted by academics having little market experience, caught on briefly in the 1980's. The idea was for investors to allocate more capital to stocks as they rose on a short term basis, and pull money away from them quickly as they declined. Any experienced market practitioner knows this is a bad idea, since the stock market is short-term reactionary by nature. Sure enough, portfolio insurance incurred large whipsaw losses soon after it began. Investor gave up on it right away with nothing to show for it but loses. 

Momentum, on the other hand, has always made sense. It is based on the phrase "cut your losses; let your profits run on," coined by the famed economist David Ricardo in the 1700s. Ricardo became wealthy following his own advice. Many others, such as Livermore, Gartley, Wycoff, Darvas, and Driehaus, have done likewise over the following years. Behavioral finance has given solid reasons why momentum works. The case for momentum is now so strong that two of the fathers of modern finance, Fama and French, call momentum "the premier market anomaly" that is "above suspicion."

The second criterion for accepting a new investment approach is robustness. One way to judge this is by a model's complexity. Simpler is better. Fewer moving parts means fewer unanticipated consequences, and less danger of model over specification.Over fitting data by adding complexity to a model can also make it too rigid. It may then perfectly predict the past, but not the future.

Momentum, on the other hand, is pretty simple. Every approach, including momentum, must determine what assets to use and when to rebalance a portfolio. The single parameter unique to momentum is the look back period for determining an asset's relative strength. In a 1937, using data from 1920 through 1935, Cowles and Jones found stocks that performed best over the past twelve months continued to perform best afterwards. In 1967, Bob Levy came to the same conclusion using a six-month look back window applied to stocks from 1960 through 1965. In 1993, using data from 1962 through 1989 and rigorous testing methods, Jegadeesh and Titman (J&T) reaffirmed the validity of momentum. They found the same six and twelve months look back periods to be best. Momentum is not only simple, but it has been remarkably consistent over the past seventy-five years.

The opposite problem of too much complexity is omitted variable risk. For a model to be robust, it needs to incorporate all relevant explanatory variables. As Einstein pointed out, a model should be as simple as possible, but not simpler. Perhaps the most dramatic example of omitted variable risk is the case of Long Term Capital Management (LTCM). Academics again sold the investment community on what at first appeared to be a good idea – exploiting anomalies identified through equilibrium-pricing models. The omitted variable in this case was the potential risk from a combination of high leverage and low liquidity. By ignoring this, LTCM almost brought about a collapse of the world's financial system.

Momentum, on the other hand, seems safe from omitted variable risk. Momentum does not depend on esoteric markets, derivatives, leverage, or anything out of the ordinary. As seen in my latest research paper, momentum has been highly effective when applied simply to the world's most liquid markets and most well-known asset classes.   

The final way of judging robustness is by seeing how well an approach holds up in multiple markets, over different time periods, and with different parameter values. Risk Parity (RP) is popular based on its attractive pro-forma performance record over the past ten years. RP puts an emphasis on fixed income assets, which have done very well over this period. However, it is not logical for bonds to outperform equities indefinitely, since stocks are riskier than bonds and should command a long-term positive risk premium. In line with this, RP portfolios are not as attractive when looking at pro-forma portfolios going back more than twenty years.

Momentum, on the other hand, is one of the most robust approaches in terms of its applicability and reliability. Following the 1993 seminal study by J&T, there have been nearly 400 published momentum papers, making it one of the most heavily researched finance topics over the past twenty years. Extensive academic research has shown that price momentum works in virtually all markets and time periods, from Victorian ages up to the present.

The final way to judge investment worthiness is through real-time performance. This is often the primary criteria used to evaluate investments. On its own, however, it has drawbacks. First, the time to establish statistically meaningful results is longer than most people realize. Ken French has said that seventy years of past performance data may sometimes not be a sufficiently long performance record. Some analysts believe they are being diligent by requiring a one, three, or five year real time track record before they will consider an unfamiliar investment opportunity. However, as disclosure requirements point out, past performance may not be indicative of future results. This is especially true of shorter term real time track records, and when used with complicated investment models.

When someone asks me how long momentum has been around, I point out the Cowles and Jones research findings from seventy-five years ago and the Levy results from forty-five years ago, along with the subsequent validation of their work. This simple, proven approach is the underlying basis for momentum investing, even now.

More Time Series Momentum...

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My use of absolute momentum is a key factor in my latest momentum paper. Positive absolute momentum exists when an asset shows a positive excess return over the look back period. Others may call this time series momentum. The more common momentum approach, which appears in most research papers, is cross sectional (or relative strength) momentum, where one asset is compared to its peers, and the strongest is selected. Based on my research, cross sectional and time series momentum make for a great combination.

In September of last year, Moskowitz et al wrote a detailed working paper on time series momentum. It showed up on SSRN in June 2012, and I did a blog post calling attention to this paper. Since then, there have been several other working papers dealing with time series momentum. One of the more interesting ones appeared on SSRN earlier this month. It is called Improving Time Series Strategies: The Role of Trading Signals and Volatility Estimators, by Akindynos-Nikolaos Baltas and Robert Kosowski. In it, the authors look at the implications of trading signals and volatility estimators on the profitability of monthly time series momentum strategies. Last December, the authors issued a paper called Momentum Strategies in Futures Markets and Trend-Following Funds, in which they looked at time series momentum patterns across monthly, weekly, and daily frequencies of commodity contracts..

The authors go on to compare various ways of identifying time series momentum. These include whether an asset has been up or down over the look back period, a moving average trend identifier, and several methods based on the t statistic of the regression slope. The most complicated method looks at 30 minute time periods, as well as daily data, in order to add an R Squared cut-off filter. Based on the Ziemba Sharpe ratio, this method looks attractive. However, it also has the highest volatility. Looking at the performance chart of all the methods, the simple up/down method seems to be the most consistent.

I found one of the most interesting parts of their recent paper to be their exploration and comparison of different volatility estimators. These could be useful for risk parity style asset allocations, which I may address in another post.

Global Balance Momentum Index (GBMI)

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Our Balanced Momentum Index (BMI) shows that a combination of absolute and relative momentum can give attractive past results using only four assets (Russell 1000, US REITs, Treasury, and high yield bonds) plus Treasury bills. Another reason I introduced the BMI is that people have been wanting to construct investment portfolios using the four-module approach in my last momentum paper. I wrote my momentum papers to illustrate certain points about momentum investing and not as models for actual investing. In my opinion, something like the BMI is better suited for that purpose. It was designed as an investable benchmark for US-based assets.

For those attracted to a more global approach, I am now introducing the Global Balanced Momentum Index (GBMI). It uses the same methodology and assets as the BMI, but adds global equities, REITs, and government bonds. Here are the pro-forma past performance summaries of the two indices from January 1984 through August 2012:
 



 GBMI   BMIGlobal 60/40  60/40
Annual Return 14.89  14.02     10.07  10.40
Standard Deviation   8.25    8.15     10.28    9.84
Sharpe Ratio   1.21    1.13       0.55    0.60
Month End Max Down-10.86 -12.85    -33.59 -32.66



Past performance is no assurance of future profitability. See the website disclaimer page for additional notes.

I now include monthly past performance and NAV updates of both the BMI and the GBMI on my website research page.








































New Webpage - Momentum Background

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We have a new page called Momentum Background added to our website  It gives a brief overview of the history of momentum investing and momentum research findings, There are also links to other sources of information on momentum, and to our own research.

Dual Momentum

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I've completed a rewrite  and expansion of my last momentum paper. It has more information on absolute momentum and more emphasis on the benefits of dual momentum. I also gave it a new title: Risk Premia Harvesting Through Dual Momentum. You can download it now from SSRN by clicking here. I hope to have a new research paper on absolute momentum out soon. 
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