October 17, 2016

Book Review of Quantitative Momentum

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 once one becomes a momentum investor. They can keep momentum from being over exploited.

In Chapter 1 W&J give a short history of trend based and 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. 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 get fired. So anomalies like momentum do not get arbitraged away.

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 under performance.
To prepare investors for difficult times, W&J highlight the risks associated with value and momentum investing. They point to Julian Robertson’s Tiger Funds that lost almost all their clients by sticking to their value model in the late 1990s. Value under performed the market in 5 out of 6 years then, sometimes by double digits. W&J make 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 risk is 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.

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 show that momentum, like value, can under perform over long periods. They point out a 5-year stretch when momentum under performed the broad market by 15%. This could be challenging to any stock momentum 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 may also be worth noting is the decline over time of both value and momentum premia.
In Chapter 5 W&J show that momentum is an intermediate term anomaly. Stock momentum works best using a 3 to 12-month look back period. W&J use 12-months for their quantitative momentum approach 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.

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

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. But most investors draw upon the same universe of momentum stocks. Alpha Architect even shows the top 100 momentum stocks on their website each month to those who register 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 an issue. W&J mention a paper called "The Illusionary Nature of Momentum Profits" in the Journal of Financial Economics by Lesmond, Schill, and Zhou (2002). Lesmond et al. conclude that after transaction costs, momentum profits are largely illusionary. W&J also mention research by Korajczyk and Sadka (2004) in the Journal of Finance showing that stock momentum has a limited capacity of only about $5 billion.

Offsetting these arguments, W&J present the working paper findings by Frazzini, Israel, and Moskowitz (2014) of AQR. Frazzini et al. argue that momentum trading costs are manageable using some kind of optimized trading of AQR’s own proprietary data from 1998 through 2011, if one is willing to accept added tracking error.

In their study of momentum, 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. with both monthly and quarterly rebalancing.
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 under react to continuous information. They 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 both 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 below 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 some 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 it 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 as replacements for the stocks they sell. This can create some abnormal profits in these stronger stocks.

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 ending 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 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 should continue to have behavioral biases.  Investors are also short-sighted performance chasers. These characteristics should keep them from over exploiting the momentum anomaly.

To reinforce this point, W&J say, “… strategies like value and momentum presumably will continue to work because they sometimes fail spectacularly relative to passive benchmarks.” This may not be great news for those who at that time hold momentum or value stocks. But W&J offer these words of wisdom and 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 summarize their recommended approach by showing 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 under performance.

But volatility and drawdowns are still high. So 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 broad stock indices rather than to portfolios of 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. As I show in my book, trend following added to momentum using broad-based indices can lead to higher rather than lower expected returns. 

W&J finish up by again referring to relative performance risk. This is a good thing to do if you promote volatile strategies. One cannot stress often enough the warning 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, you should not confuse absolute strength with absolute momentum. Otherwise, their analysis here is first rate.

Overall, 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.” Momentum with broad-based indices can substantially lower transaction costs and scalability issues. It also responds better to trend-following risk-reduction overlays, such as absolute momentum.

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

September 16, 2016

Factor Investing: Buyers Beware

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 and Pontiff (2015) looked at 97 factors from academic literature that predicted cross-sectional stock returns. They found that factor returns were 58% lower 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 found a 32% decay in average factor 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 substantial 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 Morningstar designated benchmarks from their beginnings 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 underperformed small 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.

Vanguard value temporarily outperformed growth from 2004 to 2008, but growth outperformed value by 0.6% per year since the end of 1992. In summary, with respect to 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. Serious 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 momentum and made value redundant in 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. As Benoit Mandelbrot once said, "Many a grand theory has died under the onslaught of real data."
[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.

August 29, 2016

Risk Tolerance Assessment

(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:
  1. Risk aversion
  2. Risk capacity
  3. 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 would accept 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. This can 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 often trade $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.
Other firms 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 to their risk tolerances. In such cases, investors might 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.

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.

[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.

August 4, 2016

Most Useful Investment Blogs

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 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.

June 13, 2016

Smart Beta Is Still Just Beta

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) should 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 U.S. equities are near an 8-year low, and cash levels are at their highest level in 14 years, according to the latest Bank of America Merrill Lynch Global Fund Manager Survey.

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 just 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. The P/B ratio of USMV is 3.2 versus 2.5 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 outperformance relative to cap-weighted benchmarks from 2000 through 2014 can be traced to systematic risk-factor exposures. After accounting for market, size, and value risk factors, none of the smart beta strategies showed results that were significantly different from zero. In most cases these strategies produced negative excess returns after accounting for their risk-factor exposure.
Source: "An Evaluation of Smart Beta and Other Rules-Based Active Strategies", Vanguard Research, August, 2015

Glushkov (2015) 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 risk factors. He also found  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.
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.

Keep in mind that cap-weighted indexes have a built in momentum slant 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).