THE EFFICIENT MARKET MAFIA KILLS RELATIVE STRENGTH

As we alluded to in Chapter 2, the efficient market hypothesis (EMH) was developed at the University of Chicago in the 1960s and 1970s. The EMH hypothesis subsequently flourished across academia. Under the semi-strong form interpretation of the EMH, asset prices reflect all publicly available information so that there is no way for investors to consistently outperform a randomly selected basket of securities after controlling for risk. Or as EMH proponent Burton Malkiel so eloquently put it in his 1973 classic, A Random Walk Down Wall Street: “A blindfolded monkey throwing darts at a newspaper's financial pages could select a portfolio that would do just as well as one carefully selected by experts.”³ Thus, from an EMH perspective, for all intents and purposes, Levy's evidence on the performance of relative strength strategies was an impossibility.

It seems that practitioners like Levy (who worked in the private sector at the time his paper was published), were overtaken by the cult of academics focused on pursuing the efficient market hypothesis. Practitioners were essentially banned from publishing in top-tier academic finance journals and academics pursuing research interests that went counter to the EMH idea were driven from the emerging EMH temple.⁴ The subsequent 25 years of published academic research entered a dark age, and discussions of relative strength strategies were effectively banned, since space was primarily reserved for EMH cheerleaders.

Yet, all was not well in the ivory tower. Anomalies that were inconsistent with EMH began to emerge in the literature in the 1970s. For example, as previously mentioned, Ben Graham, among others, had shown that buying a basket of cheap stocks tended to outperform the market, and academics began to formally examine the value effect. Evidence related to value and other so-called anomalies began to accumulate, hinting that there might be kinks in the EMH armor, but EMH proponents remained confident. However, around the same time that many EMH supporters were basking in glory, Daniel Kahneman, working with Amos Tversky, started exploring how human biases affected financial decision making. Kahneman and Tversky established some of the earliest connections between investors' internal behavioral biases and many of the observed anomalies that were being identified in the academic finance literature.

“MOMENTUM” RISES FROM THE ASHES

Finally, in the early 1990s, Narasimhan Jegadeesh and Sheridan Titman revitalized the findings from Levy's 1967 paper in their pioneering 1993 article “Returns to Buying Winners and Selling Losers: Implications for Market Efficiency.” This paper essentially replicated the spirit of the analysis conducted by Levy in 1967, but with the benefit of more data, computational power, and willingness on behalf of the establishment to publish research that questioned EMH. By now, the cracks in the EMH armor were getting bigger.

Interestingly enough, Jegadeesh and Titman never mention the word momentum in their original paper, even though their paper is considered by many to be the seminal work on modern-era stock selection momentum strategies. We posit that the term momentum was adopted after Mark Carhart published his University of Chicago dissertation in The Journal of Finance in 1997. In this paper, Carhart creates a momentum factor, which essentially reflected the relative strength of the stock selection strategies outlined in Jegadeesh and Titman's paper.⁵ Soon after Carhart's paper, momentum became the new academic term for the age-old relative strength strategy. With the floodgates open, researchers published a flurry of papers on momentum strategies. The evidence was so overwhelming that the anomaly was crowned the “premier anomaly” by none other than Eugene Fama—an original architect of the EMH theory.⁶

Remarkably, while modern-day academics refocused on the concepts of stock selection momentum, many practitioners continued to be stuck in a time warp. The reasons for this regressive behavior are likely related to the practitioner training pipeline. The academics, who train all the MBAs that go on to manage portfolios, were still being taught portfolio mathematics so they could solve asset allocation decisions. Stock picking training was a waste of time because it was a sucker's game under a strict interpretation of the EMH. And of course, for the MBA “rebels,” there was always the value anomaly to pursue, which had been popularized by the intense success of Warren Buffett, the folksy investment hero from Omaha, Nebraska. Unlike value, however, there were no vibrant champions for momentum investing—no Ben Graham, no Warren Buffett. To make matters worse, the heroes associated with the value investing school of thought were, ironically, agreeing with the EMH academics when it came to momentum. Their value investing approach was perfectly reasonable, but momentum investing was deemed a black art, a kind of voodoo magic, only practiced by fools and heretics. Of course, all of this flew in the face of the actual evidence, which suggests that momentum investing is an even better anomaly than value investing.⁷

EMH enjoyed great success for many years, with reams of academic papers showcasing how efficient markets had become. In many respects, EMH had won the argument—prices are generally efficient. But this price efficiency is why the evidence on momentum investing was so disheartening to the EMH school of thought. The value anomaly was one thing—perhaps investors could beat the market if they used their intellect, did their in-depth homework, and understood the financial statements better than the next investor. But the momentum anomaly was saying something completely different: Price momentum had nothing to do with fundamentals, so even a halfwit could pursue a successful strategy focused solely on relative price performance, since this simple metric seemed to predict future prices. This finding conflicted with even the weakest form of EMH. Houston we have a problem.

BEHAVIORAL FINANCE THEORISTS EXPLAIN MOMENTUM

To the credit of academic researchers, the financial economics field moved forward and the behavioral finance paradigm arose, phoenix-like, from the ashes of EMH. This new paradigm held tightly to the EMH as a baseline hypothesis, but relaxed assumptions regarding investor rationality and frictionless markets, in order to understand and explain how and why prices might deviate from their efficient levels. This framework laid the foundation for the sustainable active investing concepts outlined in Chapter 2.

Hard-core value investors have a different kind of angst regarding momentum, a kind of anxiety that is grounded in clouded reasoning and a religious zeal, as is evidenced by quotations from value investing books and personas. For example, Warren Buffett is reported to have said, “The dumbest reason in the world to buy a stock is because it's going up.” As a rule of thumb, Buffett's advice isn't a bad rule, and Warren Buffett is clearly an extraordinary investor whose insights are worthy of investigation. But rules of thumb don't always capture the nuance of a situation. Higher prices may not always be an unreliable signal. For example, what if the intrinsic value of a stock is higher than the new, higher price—is that not still a value investment? Or perhaps there is a genuine positive feedback loop associated with higher prices that in turn increases the intrinsic value of the firm? High price movements may lower the cost of capital for a firm, allow them to attract better human capital, or even generate free advertising, thus increasing fundamental value, albeit in a reflexive way. In short, growth stocks, defined as stocks with high prices to fundamentals, are generally a bad thing, but higher prices, per se, aren't always a bad thing. In fact, they are generally a positive development, all else equal.

Consider the following hypothetical scenario:

• Facebook has gone up 100 percent the past year and has a price to earnings ratio of 15.
• Google has gone down 50 percent and has a price to earnings ratio of 15.

Which is the better buy? For a classic value investor, these stocks are arguably the same from a valuation perspective since they both have the same price to earnings ratio of 15. However, based on psychology, some investors will “feel” like Google is a better opportunity, since value stocks are often those that have declined in price. What value investor wants to buy a stock that is up 100 percent? True value investors are genetically programmed to be suspicious of strong upward price moves—we know because we are value investors by nature! Strong upward price movements are typically a bad signal when it comes to a traditional value or distressed investing opportunity. Upward moves suggest things are not as cheap as they were before, and are more expensive now, at least comparatively. But this feeling of disgust that is associated with buying a high-flying stock is not specific to value investors; this distrust is also felt more generally by all investors—nobody wants to be the sucker that bought after the price moved higher. In fact, people can feel a contrary urge. If you own a stock that has gone up in value, you may seek to realize the gain by selling it—after all, it feels good to realize a gain. This effect is often termed the disposition effect. There is strong empirical evidence to support the theory that the disposition effect is related to the momentum anomaly.⁸

Consider a stock that is at a 52-week high—many investors interpret this to mean the stock is overvalued and unlikely to go higher, even if it may still be cheap on a fundamental basis. The mainstream interpretation is patently false: 52-week-high stocks greatly outperform 52-week-low stocks.⁹ But if many market participants have these kinds of biases, a reasonable hypothesis is that there will be price pressure—unrelated to fundamentals—that may prevent a security from reaching its true fundamental value because market participants perceive that, for some gut-based reason, “the stock has already gone up too much.” This situation would be a case where momentum investing is essentially a cousin, not an enemy, of value investing. How so? Value investing's edge is often characterized as pessimism in the presence of poor short-term fundamentals, which causes stocks to become too cheap relative to future expectations. Perhaps momentum investing's edge could be characterized as pessimism in the presence of strong short-term fundamentals, which causes stocks to remain too cheap to future expectations.

WAIT A MINUTE: MOMENTUM INVESTING IS JUST GROWTH INVESTING, WHICH DOESN'T WORK!

Hold on, if we are arguing that stocks can be cheap due to pessimism related to strong short-term fundamentals, isn't that … growth investing?

No.

But before clarifying, let's review the psychology behind value versus growth. In our prior discussion of value and growth, the evidence showed that value beats growth. The reason for this spread is partly attributable to mispricing from behavioral bias in the market. For example, the original Lakonishok, Shleifer, and Vishny study mentioned in Chapter 2 showed that price to fundamental ratios serve as a proxy for expectation errors exhibited in the market. Recall that investors thought high past earnings growth rates would continue for growth stocks, and low past earnings growth rates would continue for value stocks. The evidence showed this expected result did not in fact occur. Follow-on studies debate this core result from Lakonishok, Vishny, and Shleifer's original findings,¹⁰ but these papers fail to address more recent work, including Daniel and Titman's 1997 paper on value characteristics and stock returns, Piotroski and So's 2012 paper on the interaction between value investing returns and fundamentals, and Jack's 117-page dissertation, which is a deep-dive into the concepts outlined in Piotroski and So's work. These more recent papers confirm that the value anomaly, while volatile and costly to exploit, is likely driven, in part, by mispricing.¹¹

Thus, on average, investors seem to over-extrapolate good news from growth firms (firms with high price to fundamentals), driving them above intrinsic value, and do the opposite with value firms (firms with low price to fundamentals), driving them below intrinsic value. So in the value-investing framework, growth investors seem to be too optimistic, given strong fundamentals. But are we now saying that momentum investors are too pessimistic, given strong prices? These positions seem to be in conflict, but we will explain.

Let us be clear: Momentum investing is not growth investing. Growth investing, in accordance with the studies mentioned, is characterized by securities that have high prices relative to past fundamentals (e.g., price-to-earnings ratio). We acknowledge that there are many alternative ways to define growth investing in practice (e.g., growth at a reasonable price), but we will stick with the academic convention for the purposes of our argument. In contrast to growth, we characterize momentum investing as securities that have strong relative performance to all other securities, independent of fundamentals. For example, a momentum strategy might consider the cumulative returns of prices over the last 12 months relative to other stocks, but earnings, or any other fundamental metric, would play no part in the analysis. To paraphrase Vince Lombardi: With momentum, prices aren't everything; they are the only thing.

We will argue that strong momentum signals, similar to low price to fundamental ratios (i.e., value measures), are a proxy for investor expectation errors, and help an informed investor systematically identify situations where behavioral bias is preventing securities, on average, from reaching perceived fundamental value. Think back to the poor poker players in the sustainable active investing framework in Chapter 2. As a first step in identifying sustainable active strategies, we need some market participants to be less than rational in order to create a mispricing opportunity. We will come back to “poor poker players” and the mechanics of how and why momentum works later. However, this point regarding the difference between the signal that characterizes momentum (i.e., price-only) versus growth (i.e., price relative to some fundamental) is extremely important to understand to ensure that readers are not confused and think that growth investing is the same thing as momentum investing.

The best way to make the point clear is with data. We examine the overlap between a portfolio of mid-to-large market capitalization firms selected on a generic momentum signal (top decile of firms with relative strongest 12-month performance, skipping the previous month) and a portfolio of firms selected based on a generic price to fundamental signal (top decile of firms with the highest price-to-book ratio—or alternatively, the book-to-market ratio, otherwise known as growth firms) for the period between 1963 and 2013. Surprisingly, there is only a 21 percent overlap between the names in the high momentum portfolios and the names in the growth portfolio. Thus, many momentum stocks are not growth stocks, and many growth stocks are not momentum stocks. In fact, a high momentum stock can be a value stock, a growth stock, or anything in between.

DIGGING DEEPER INTO GROWTH VERSUS MOMENTUM

In the following analysis we dig a little deeper into the characteristics of growth firms and high momentum firms. Our data sample includes all firms on the New York Stock Exchange (NYSE), American Stock Exchange (AMEX), and NASDAQ with the required data on CRSP and Compustat, which are the academic gold standard for financial data analysis. We only examine firms with ordinary common equity on CRSP and eliminate all REITS, ADRS, closed-end funds, utilities, and financial firms. We incorporate CRSP delisting return data using the technique of Beaver, McNichols, and Price.¹² To be included in the sample, all firms must have a non-zero market value of equity as of June 30 of year t. We use book to market (B/M) as our annual indicator of “valuation” based on academic convention. Book is computed on June 30 each year using the methodology from Fama and French¹³ and the market capitalization on June 30. All firms with negative book values are eliminated from the sample. We consider “growth” firms to be those with the most expensive B/M ratios (i.e., lower is more expensive). We calculate generic momentum by ranking all stocks monthly on their cumulative 12-month returns, skipping the most recent month, similar to Fama and French.

The tests are focused on all mid- and large-cap stocks, defined as stocks with a market capitalization above the NYSE 40^th percentile for market capitalization. This approach seeks to determine if the empirical results are applicable to the broader universe of stocks and are robust to size and liquidity effects over time. Our choice to focus on more liquid firms means that our conclusions may not be applicable to small illiquid firms.

We follow a simulation approach that works as follows:

• Each month we randomly draw 30 “growth stocks” and 30 “momentum” stocks from our top decile of growth stocks and high momentum stocks.
• Repeat every month from 1963 to 2013 to create a monthly rebalance portfolio of “growth” stocks and a monthly rebalanced portfolio of “momentum” stocks.
• Calculate performance statistics on the growth strategy and the momentum strategy from 1963 to 2013.
• Repeat the above steps 1,000 times.

The experiment above is equivalent to having a monkey, focused on growth stocks, throw 30 darts at the growth stock dartboard every month for 50 years, and another monkey, focused on momentum stocks, which will throw 30 darts at the momentum stock dartboard every month for 50 years. We'll then have both the growth and the momentum monkey do this exercise 1,000 times, so at the end we will have a sample of 1,000 separate portfolio manager monkeys from each camp.

Now some monkeys will perform well, and others will perform poorly, simply based on luck. But recall that momentum monkeys always make their picks the top momentum stock decile, and growth monkeys always make their picks from the top growth stock decile.

First, Figure 3.1 shows the distribution of compound annual growth rates for the growth monkeys and the momentum monkeys.

Relative to their monkey peers, some of the luckiest growth monkeys did well, averaging approximately 14 percent over the period, and some very unlucky momentum monkeys did poorly, averaging approximately 17 percent over the period. However, incredibly, over the 50-year period there isn't a single dart-throwing growth monkey who outperformed any dart-throwing momentum monkey. This result is stunning. Typically, when one runs a thousand simulations, one identifies some overlap in the “tails,” or extreme ends of the distribution. Clearly, from a compound return perspective, momentum is different from growth.

Next, let's look at a comparison of the volatility of the growth monkey portfolios and the volatility of the momentum monkey portfolios. Perhaps the return outperformance of momentum versus growth is compensation for extra risk associated with a generic momentum strategy.

Figure 3.2 highlights little difference in annualized volatility for the portfolios created using either a growth monkey or a momentum monkey to pick stocks. The distribution is narrow.

But perhaps volatility doesn't capture the true risk of the momentum strategy relative to the growth portfolios? We examine the worst drawdowns, or the worst peak-to-trough performance during the 50-year time period, as an extreme tail event. Figure 3.3 tabulates the extreme loss scenarios across the thousand simulations for both the growth and momentum strategies.

Note that higher drawdowns are reflected on the left side of Figure 3.3, while lower drawdowns are reflected on the right. Because the light gray bars are clustered on the left, the tail risk for growth, on average, is actually higher than for momentum, which is clustered on the right. There is some overlap—some simulation runs where momentum has larger drawdowns than growth—but these instances are few and far between. The overwhelming number of observations shows higher drawdowns for the growth monkeys than for the momentum monkeys.

To summarize, growth investing, as measured by high price to fundamentals, is not the same as momentum investing, as measured by strong past relative performance. This conclusion is clearly seen in the historical characteristics associated with each of these strategies, which shows that growth and momentum are different animals.

BUT WHY DOES MOMENTUM WORK?

“We discovered the world wasn't flat before we understood and agreed why.”

—Cliff Asness et al.¹⁴

Cliff Asness's quote highlights that sometimes you can understand that something is true before you fully understand why it is true, and agree with others why it is true. So it goes with momentum investing, where the data are clear that it works, but we lack clarity on exactly “why.” We attempt to address this conundrum, but knowingly embrace the humility that our thoughts can only hope to be directionally correct, at best. Value investing, in contrast to momentum investing, is intuitive. The value approach is intuitive because it is assumed that market prices drift around a so-called intrinsic value, which is informed by fundamentals. Classic value investors claim to earn their paycheck by timing the difference between fundamentals and market prices. But what if the market decides to never update their expectation about the intrinsic value of a firm (also known as a value trap)? Assuming free cash flow distributions are distributed in the distant future, a value investor won't win in this situation. The value investor, like all investors, needs market expectations to change in their favor for the strategy to work. Value investing doesn't work simply because the investor buys cheap. Value investing works because cheap price-to-fundamental ratios, the proxy for a systematic market expectation error, mean revert in favor of the value investor, on average. The core argument behind momentum investing works along the exact same lines. Momentum investing works because the relative strength indicator is a proxy for a systematic expectation error in the market that predictably reverts in the momentum investor's favor, on average.

To understand why momentum works, we leverage our sustainable active framework to determine if a strategy will be successful over the long term. The building blocks to identify sustainable performance were as follows: (1) identify bad poker players, (2) understand the limitations of the best poker players to exploit the bad poker players, and (3) exploit the opportunity that presents itself. We showed that value, which has a strong historical track record, has characteristics that suggest the past track record could plausibly continue in the future.

Our analysis of the value anomaly through the lens of the sustainable active investing framework raises a natural question: Is momentum, like value, a sustainable investment approach? With our sustainable active framework in hand, we can tackle this difficult question. But first, we should establish beyond any doubt that momentum—which isn't the same as growth investing—has worked, historically. To keep things simple and in line with previous analysis, we consider “momentum investing” to be roughly approximated by the practice of purchasing portfolios of firms with strong relative performance over the past year. Using the momentum portfolio data from Ken French's website,¹⁵ we examine the returns from January 1, 1927, to December 31, 2014, for a high momentum portfolio (high momentum decile, value-weight returns), a value portfolio (high B/M decile, value-weight returns), a growth portfolio (low B/M decile, value-weight returns), and the S&P 500 total return index (SP500). Results are shown in Table 3.1. All returns are total returns and include the reinvestment of distributions (e.g., dividends). Results are gross of fees.

	Momentum	Value	Growth	SP500
CAGR	16.85%	12.41%	8.70%	9.95%
Standard Deviation	22.61%	31.92%	19.95%	19.09%
Downside Deviation	16.71%	21.34%	14.41%	14.22%
Sharpe Ratio	0.66	0.41	0.35	0.41
Sortino Ratio (MAR 5%)	0.79	0.54	0.37	0.45
Worst Drawdown	–76.95%	–91.67%	–85.01%	–84.59%
Worst Month Return	–28.52%	–43.98%	–30.65%	–28.73%
Best Month Return	28.88%	98.65%	42.16%	41.65%
Profitable Months	63.16%	60.51%	59.09%	61.74%

Momentum stocks have outperformed value stocks, growth stocks, and the broader market by a large margin. The portfolio of momentum stocks earns a compound annual growth rate of 16.85 percent per year, whereas, the growth stock portfolio earns 8.70 percent per year—an 8 percent annual spread in performance. This historical spread in returns is why momentum has been deemed the premier anomaly by academic researchers. There are obviously important considerations we avoid at this stage of our discussion, such as transaction costs, but one fact is clear—momentum is the performance king. Next question, is this performance sustainable?

What Do the Best Players Think about Momentum?

Similar to value investing, momentum investing requires a level of discipline that few investors possess. Momentum does not work all the time and can fail spectacularly. This harsh reality prevents many large pools of capital from dipping their toe too far into the momentum pool. Momentum is simply too dangerous.

To make the point that momentum can sometimes be hazardous to your wealth, we examine the pain of momentum investing during the 2008 financial crisis and the follow-on period. We examine the returns from January 1, 2008, to December 31, 2009, for a momentum portfolio (high momentum decile, value-weight returns), a growth portfolio (low B/M decile, value-weight returns), a value portfolio (high B/M decile, value-weight returns), and the S&P 500 total return index (SP500). Results are shown in Table 3.2. All returns are total returns and include the reinvestment of distributions (e.g., dividends). Results are gross of fees.

	Momentum	Growth	Value	SP500
CAGR	–17.65%	–8.52%	–6.69%	–10.36%
Standard Deviation	26.03%	23.45%	45.60%	23.24%
Downside Deviation	20.67%	17.38%	23.06%	17.37%
Sharpe Ratio	–0.64	–0.30	0.05	-0.39
Sortino Ratio (MAR 5%)	–1.01	–0.64	–0.09	-0.76
Worst Drawdown	–51.25%	–46.72%	–61.04%	–47.75%
Worst Month Return	–15.19%	–16.13%	–28.07%	–16.70%
Best Month Return	11.09%	9.92%	36.64%	9.42%
Profitable Months	50.00%	54.17%	62.50%	54.17%

On a relative basis, momentum underperformed by a substantial amount. When we look at risk-adjusted statistics the performance is even worse. Clearly, following an active momentum strategy involves strong elements of investment advisor career risk, akin to active value strategies. But it gets worse …

In Table 3.3 we examine returns over the financial crisis period and we include the follow-on period: January 1, 2008, to December 31, 2014. Results are gross of fees. Simple passive index funds outperform momentum over a seven-year period!²⁴

	Momentum	Growth	Value	SP500
CAGR	6.55%	8.69%	8.45%	7.44%
Standard Deviation	22.24%	17.13%	29.73%	16.75%
Downside Deviation	17.03%	12.92%	20.78%	13.30%
Sharpe Ratio	0.39	0.56	0.41	0.50
Sortino Ratio (MAR 5%)	0.23	0.37	0.36	0.27
Worst Drawdown	–51.25%	–46.72%	–61.04%	–47.75%
Worst Month Return	–15.91%	–16.13%	–28.07%	–16.70%
Best Month Return	14.93%	11.21%	36.64%	10.93%
Profitable Months	61.90%	61.90%	59.52%	63.10%

Ask yourself the same question we posed with the results from a hypothetical value investor from 1994 to 1999:

The answer is a resounding “Yes!” for most investors, which translates into a resounding, “No way, Jose!” for professional asset managers concerned about their careers. But the market frictions associated with exploiting a momentum strategy extend beyond career risk. Unlike value, which is a strategy that works when traded relatively infrequently (e.g., annual rebalanced portfolios have excess risk-adjusted returns), momentum is a strategy that requires a higher degree of trading frequency to be effective (e.g., quarterly rebalanced portfolios have excess risk-adjusted returns, but annually rebalanced portfolios do not). This trading frequency increases transaction costs, which can be prohibitive and reduce the profitability of the strategy, net of trading costs. While a plausible limit of arbitrage, Frazzini et al. address this question directly using data from over a trillion dollars in live transactions from AQR Capital Management and find that transaction costs for efficient institutional investors cannot “explain away” their unwillingness to pursue momentum strategies.²⁵

SUMMARY

In this chapter, we explored the history of momentum research from its early days as a respectable approach, through the dark ages following the golden age of the EMH, and the more recent resurgence in academic interest. We then explore the common misperception that growth investing, as defined as buying stocks with high prices to fundamentals, is the same as momentum investing, which is buying stocks with strong relative returns. Nothing could be further from the truth. Buying expensive stocks is not the same as buying stocks with strong relative performance: one strategy performs, the other does not. Next, we investigated momentum through the lens of sustainable active investing. Momentum strategy excess expected returns are plausibly driven by investor behavioral errors, combined with limits of arbitrage, and thus reasonably support an argument for long-term sustainable performance. Assuming we have convinced you that momentum, like value, is arguably a sustainable anomaly, we now explore why these two particular anomalies are really interesting when used together. In the next chapter, we will explore why all portfolios should consider combining value and momentum systems.