Inefficient Market Mavericks: Value Investors

Wes wanted to determine if fundamental investors, or “value” investors, could beat the market. He had been religiously following a value investing strategy with his own account for over 10 years. He was a tried-and-true believer in the Ben Graham fundamentals-focused value investing religion (he still considered technical trading ideas to be heresy). The story that active value investing could beat the market was compelling, but much of the rhetoric in academic circles, and the research published in top-tier academic journals, suggested otherwise.

The value debate was reinvigorated by a highly cited Eugene Fama and Ken French paper titled “The Cross-Section of Expected Stock Returns.”² The paper sparked a debate over whether or not the so-called value premium, or the large spread in historical returns between cheap stocks and expensive stocks, was due to extra risk or to mispricing. Were the excess returns of value stocks a reward for added economic risk factors borne by shareholders, or were these stocks simply mispriced? For Eugene Fama and Ken French, the answer was clear: The value premium must be attributed to higher risk if the market was efficient. The risk-based argument for the value premium seemed far-fetched to Wes, who was a Ben Graham aficionado. Graham and his disciple Warren Buffett were famous for beating the market over long periods of time by buying cheap stocks. Their claim was that “Mr. Market,” who represented the broad market, was characterized as a manic-depressive person with deep psychological problems: Mr. Market would sometimes offer stocks for prices below their fundamental value (e.g., the trough of the 2008 financial crisis) or above their fundamental value (e.g., during the Internet bubble of the late 1990s). And if a value investor purchased cheap, eventually Mr. Market would agree. But could it be the case that the stocks these value investors bought had high returns, not because they outsmarted Mr. Market, but because they were buying more risk and got lucky? Wes began digging.

Wes started collecting data on nearly 4,000 investment picks submitted by top fund experts, asset managers, and value enthusiasts to Joel Greenblatt's website, ValueInvestorsClub.com. This club wasn't just any club. This club was highly selective, with members screened for quality, and was regarded as one of the best sites on the web for market ideas. Members tended to be heavy hitters in the value investing arena.

After a year of toil and anguish, Wes compiled all the members' stock recommendations into a database so he could conduct a thorough analysis. The results were extremely compelling—there was strong evidence that these “varsity value investors” exhibited significant stock-picking skills.

Excited to share his new findings, Wes eagerly drafted a paper, which included the following sentence at the end of the abstract:

Pleased with his work, Wes sent his draft dissertation to his adviser, Dr. Eugene Fama, who by then was widely recognized as the “father of modern finance,” and was closely identified with the efficient market hypothesis (“EMH”). Dr. Fama would go on to win the 2013 Nobel Prize in Economics. Dr. Fama was a strong—perhaps the strongest—supporter of EMH. Because Dr. Fama reviewed the results of Wes's research personally, Wes's draft was sure to be rigorously scrutinized. The response Wes received was less than ideal:

Wes sped down to Dr. Fama's office to get some clarification. The last thing Wes wanted was a year's worth of blood, sweat, and tears to get tossed out the window. Wes's evidence seemed solid. Was Dr. Fama simply being dogmatic? Wes had to know exactly why Dr. Fama disagreed. Sweating profusely, with the prospect of the PhD degree slowly slipping away, he asked one of the world's most famous financial economists for clarification. Fama responded that the data and analysis were sound, but that Wes simply couldn't say that value investors had stock-picking skills. Always a stickler for detail, Dr. Fama insisted that Wes qualify the abstract by adding two clarifying words to the concluding statement from the paper: “The sample.” So instead of saying that “value investors have stock picking skills” the final sentence needed to say that “the sample of value investors have stock-picking skills.”³

Wes sat back, relieved, and relearned what he had been taught by his mother as a young child: words matter. The eminent Fama was, not surprisingly, correct: Wes's findings did not suggest that all value investors have skill, merely that the sample he was investigating had skill—a subtle, yet important distinction. Crisis averted.

Wes graduated the following year, with his research affirming, at least for him, if not for Dr. Fama, that markets were not perfectly efficient and value investors had an edge. Soon thereafter, Wes took a job as a finance professor at Drexel University and met Jack Vogel, who was a finance PhD student at the time. Jack would go on to publish his dissertation, which suggested the extra returns associated with value stocks were likely driven by mispricing and not additional risk.

But nagging questions abounded: What gives a certain investor “edge”? What characteristics drive alpha? Why can one active investor (the winner) systematically take money from other investors (the losers)?

Enter Behavioral Finance

“[Behavioral finance] has two building blocks: limits to arbitrage…and psychology.”

—Nick Barberis and Richard Thaler⁴

As Wes plowed through thousands of stock-picking proposals, one key takeaway became clear. These analysts were good. Collectively, they had skill. They were smart. They all made compelling cases that statistically outperformed in the aggregate. But Jack's dissertation research also found that harnessing the power of a computer to buy generically cheap stocks with strong fundamentals performed about equally well as the fundamental stock pickers that Wes had investigated in his dissertation. Value investing, whether driven by a human or a computer, beat the market. But why?

As mentioned, many in the market are smart and capable—intellect alone cannot be the driver of superior returns. What enabled value investors to buy low and sell high, and why was the efficient market hypothesis not stopping them?

John Maynard Keynes was a groundbreaking early-twentieth-century economist. He also spent many years as a professional investor, and may have had the answer. Keynes was a shrewd observer of financial markets and a successful investor in his time. But even Keynes struggled as an investor. At one point, Keynes was nearly wiped out while speculating on leveraged currencies (despite otherwise being a highly successful investor). His downfall led him to share one of the greatest investing mantras of all time:⁵

Keynes's quip highlights two key elements of real world markets that the efficient market hypothesis (EMH) doesn't consider: Investors can be irrational and the attempt to exploit market mispricing, or arbitrage, is risky. We can break Keynes's quote into academic parlance: First, the phrase “… longer than you can remain solvent” speaks to the fact that arbitrage is risky and is referred to by academics as “limits to arbitrage.” Second, the “Markets can remain irrational…” component speaks to investor psychology, which is an area of research that has been well developed by professional psychologists. These two elements—limits to arbitrage and investor psychology—are the building blocks for so-called behavioral finance (depicted in Figure 2.1).

Understanding the Worst Players

All human beings suffer from behavioral bias, and these biases are magnified in stressful situations. After all, we're only human.

We laundry list a plethora of biases that can affect investment decisions on the financial battlefield:

• Overconfidence (“I've been right before…”)
• Optimism (“Markets always go up.”)
• Self-attribution bias (“I called that stock price increase…”)
• Endowment effect (“I have worked with this manager for 25 years; he has to be good.”)
• Anchoring (“The market was up 50 percent last year; I think it will return between 45 and 55 percent this year.”)
• Availability (“You see the terrible results last quarter? This stock is a total dog!”)
• Framing (“Do you prefer a bond that has a 99 percent chance of paying its promised yield or one with a 1 percent chance of default?”—hint, it's the same bond.)

The psychology research is clear: humans are flawed decision makers, especially under duress. But even if we identify poor investor behavior, that identification does not necessarily imply that an exploitable market opportunity exists. As discussed previously, other smarter investors will surely be privy to the mispricing situation before we are aware of the opportunity. They will attempt immediately to exploit the opportunity, eliminating our ability to profitably take advantage of mispricing caused by biased market participants. We want to avoid competition, but to avoid competition we need to understand the competition.

Understanding the Best Poker Players

In the context of financial markets, the best pokers players are often those investors managing the largest amounts of money. These market participants are exemplified by the hedge funds with all-star managers or institutional titans running massive fund complexes. The resources available to these investors are remarkable and vast. One can rarely overpower this sort of opponent. Thankfully, overwhelming strength isn't the only way to slay Goliath. One can outmaneuver these titans because many top players are hamstrung by perverse economic incentives.

Before we dive into the incentives of these savvy players, let's quickly review the concept of arbitrage. The textbook definition of arbitrage involves a costless investment that generates riskless profits, by taking advantage of mispricings across different instruments representing the same security (think back to our orange example). In reality, arbitrage entails costs as well as the assumption of risk, and for these reasons there are limits to the effectiveness of arbitrage. There is ample evidence for such limits to arbitrage. Examples include the following:

1. Fundamental Risk. Arbitragers may identify a mispricing of a security that does not have a perfect substitute that enables riskless arbitrage. If a piece of bad news affects the substitute security involved in hedging, the arbitrager may be subject to unanticipated losses. An example would be Ford and GM—similar stocks, but they are not the same company.
2. Noise Trader Risk. Once a position is taken, noise traders may drive prices farther from fundamental value, and the arbitrageur may be forced to invest additional capital, which may not be available, forcing an early liquidation of the position.
3. Implementation Costs. Short selling is often used in the arbitrage process, although it can be expensive because of the “short rebate,” which represents the costs to borrow the stock to be sold short. In some cases, such borrowing costs may exceed potential profits. For example, if short rebate fees are 10 percent and the expected arbitrage profits are 9 percent, there is no way to profit from the mispricing.

The three market frictions mentioned are important. There are potentially many others, but the biggest risk for most smart players is the balance they must strike between long-term expected performance and career risk. An explanation is in order. The biggest short-circuit to the arbitrage process are the limits imposed on smart fund managers that face short-term focused performance assessments. Consider the pressures produced by tracking error, or the tendency of returns to deviate from a standard benchmark. Say a professional investor has a job investing the pensions of 100,000 firemen. They have a choice of investment strategies. They can invest in the following options:

• Strategy A: A strategy that they know (by some magical means) will beat the market by 1 percent per year over 25 years. But, they also know that this strategy will never underperform the index by more than 1 percent in a given year; or
• Strategy B: An arbitrage strategy that the investor knows (again, by some magical means) will outperform the market, on average, by 5 percent per year over the next 25 years. The catch is that the investor also knows that they will have a 5-year period where they underperform by 5 percent per year.

Which strategy does the investment professional choose? If they are being hired on behalf of 100,000 firemen, the choice is often obvious, despite being sub-optimal for their investors: choose Strategy A and avoid getting fired!

Why choose A? This strategy is a bad long-term strategy relative to B. The incentives of an investment manager are complex. Fund managers are not the owners of the capital, but work on behalf of someone who does. Financial mercenaries, if you will. These managers sometimes make decisions that increase the odds of them keeping their job, but will not necessarily maximize risk-adjusted returns for their investors. For these managers, relative performance is everything and tracking error is dangerous. In the example above, the tracking error on Strategy B is just too painful to digest. Those firemen are going to start screaming bloody murder during the five years of underperformance, and the manager will not be around long enough to see the rebound when it occurs after year 5. But if the manager follows Strategy A, he can avoid career risk and the fireman's pension will not endure the stress of a prolonged downturn.

Over long time frames, a mispricing opportunity may be a mile wide—you could drive a proverbial truck through it. But this agency problem—the fact that the owners of the capital can, in the short-term, begin to doubt the abilities of the arbitrageur and pull their capital—precludes smart managers from taking advantage of the long-term mispricing opportunities that are highly volatile.

The threat of short-term tracking-error is very real. Consider the commonly cited example of Ken Heebner's CGM Focus Fund.¹⁰ A Wall Street Journal (WSJ) article offers some facts relating to Ken's fund performance:

Next, the WSJ lays out additional facts relating to the performance of investors in Ken's fund:

Ken's fund compounded at 18 percent a year, and yet, the investors in the fund lost 11 percent a year, a reflection of the typical investor's inability to time effectively in and out of Ken's fund (see Figure 2.3).¹¹ When Ken's fund was underperforming (and the opportunity was high), they pulled capital; when his fund was outperforming (and opportunity was low), they invested more capital. On net, Ken looks like a genius, but few investors actually benefited from Ken's ability—a lose-lose proposition.

Ken's Heebner's experience highlights this conflict of interest problem for asset managers. The dynamics of this problem are explored in an illuminating 1997 Journal of Finance paper by Andrei Shleifer and Robert Vishny, appropriately called “The Limits of Arbitrage.”¹² The takeaway from Ken Heebner's experience and Shleifer and Vishny's insights is as follows: Smart managers avoid long-term market opportunities if their investors are focused on short-term performance.

And can you blame the managers? If their careers depend on their relative performance over a month, a year, or even every five years, then asset managers will clearly care more about short-term relative performance than about long-term expected risk-adjusted returns. Whether they are proactively protecting their jobs or the clients are actively driving the conversation around near-sighted metrics, the end result is the same. Fund investors lose, and prices are not always efficient.

Keys to Long-Term Active Management Success

“There are a lot of smart people…so it's not easy to win.”

—Charlie Munger, Vice Chairman Berkshire Hathaway¹³

We've outlined a few elements of the marketplace. First, some investors are probably making poor investment decisions, and second, some managers are unable to exploit genuine market opportunities due to incentives. We encapsulate these elements in a simple equation for sustainable long-term performance in Figure 2.4.

The long-term performance equation has two core elements:

• Sustainable alpha
• Sustainable investors

Sustainable alpha refers to an active stock selection process that systematically exploits mispricings caused by behavioral bias in the marketplace (i.e., finds the worst poker players). In order for this “edge” to be sustainable, it cannot be arbitraged away in the long run. Typically, sustainable edges are driven by strategies that require a long-horizon and indifference to short-term relative performance in order to be successful. That requirement brings us to our second element of the long-term performance equation: sustainable investors. Sustainable investors cannot fall victim to the siren song of short-term underperformance. If they do fall prey to short-termism, these unsustainable investors will greatly enhance the arbitrage costs for their delegated asset manager, and will thus prevent the investors from profitably exploiting mispricing opportunities.

Based on the equation, if one can identify a process with an established edge (i.e., sustainable alpha) that requires long-term discipline to exploit (i.e., requires sustainable investors), it is likely that this process will serve as a promising long-term strategy that will beat the market over time.

Moving from Theory to Practice

Much of this discussion outlines an intellectual framework for successful active investing. There is no discussion of whether value investing is better than growth investing, or if high-frequency trading is better than investing in pork belly futures. However, the building blocks to identify sustainable performance are simple to follow:

• Identify a sustainable alpha process that can exploit bad players.
• Understand the limitations of good players.
• Exploit the opportunity by pairing a good process (that good players avoid exploiting) with sustainable capital.

To put a little bit of meat on the bone, we provide an example of how this construct works in the “value versus growth” debate, which is a familiar discussion for most readers. To keep things simple and in line with academic research practices, we consider value investing to be approximated roughly by the practice of purchasing portfolios of firms with low prices to some fundamental price metric (e.g., a high book-to-market or B/M ratio). Growth investing is the opposite approach—purchase firms with high prices relative to fundamentals, with the expectation that fundamentals will grow rapidly. Using Ken French's data,¹⁴ we examine the returns from January 1, 1927, to December 31, 2014, for a value portfolio (high B/M decile, value-weighted returns), a growth portfolio (low B/M decile, value-weighted returns), and the S&P 500 total return index. By value-weight, we mean that each stock is given its weight in the portfolio, depending on the size of the firm. Results are shown in Table 2.1. All returns are total returns and include the reinvestment of distributions (e.g., dividends). Results are gross of fees.

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

The historical evidence is clear: value stocks from 1927 to 2014 have outperformed growth stocks—by a wide margin. The portfolio of value stocks earns a compound annual growth rate of 12.41 percent per year, whereas, the growth stock portfolio earns 8.70 percent per year—approximately a 4 percent annual spread in performance. This historical spread in returns, which has been repeatedly and consistently observed over time, has been labeled the value anomaly by academic researchers. Of course, academics argue over the reasons why the spread is large (e.g., value investing might earn higher returns because it is simply more risky or because of mispricing, as discussed earlier). This debate is best captured by a 2008 interview with Eugene Fama where he describes a personal conversation with Andrei Shleifer over a glass of wine.¹⁵ Fama highlights that Andrei believes the value premium is due to mispricing, whereas Fama attributes the value premium to higher risk. Bottom line: Great minds can disagree on the explanation, but nobody can dispute the empirical fact that value stocks have outperformed growth stocks by a wide margin over time.

We Have the Facts. Next Step: Identify Bad Players

The data highlight that value investing has higher expected returns than growth investing. But to better understand whether value will beat growth in the future we need to look through the sustainable active investing prism and identify if the spread is due to risk (the efficient market explanation) or mispricing (the behavioral finance explanation). For a valid mispricing argument, we need to identify if there are market participants making systematically poor decisions with respect to the purchase of value and growth stocks.

Lakonishok, Shleifer, and Vishny (LSV) explore this question in their paper “Contrarian Investment, Extrapolation, and Risk.”¹⁶ LSV hypothesize that investors suffer from representative bias, a situation where investors naively extrapolate past growth rates too far into the future. Figure 2.5 highlights the concept from the LSV paper using updated data from Dechow and Sloan's 1997 paper, “Returns to Contrarian Investment Strategies: Tests of Naive Expectations Hypothesis.”¹⁷ The horizontal axis represents cheapness and sorts securities into buckets, from left to right, based on whether stocks are expensive (low book-to-market ratios) or cheap (high book-to-market ratios). The vertical axis represents past five-year earnings growth rates for the respective valuation buckets. Stocks in Bucket 10 are the cheapest, and they exhibited (on average) a negative 1 percent earnings growth over the preceding five years.

The relationship is almost perfectly linear. Cheap stocks have terrible past earnings growth, whereas expensive stocks have had wonderful earnings growth over the past five years. No real surprise there, but it is interesting to see how well the data fits this relationship.

Figure 2.5 underscores the general market expectation that past earnings growth rates will continue into the future. Growth firms are expensive because market participants believe past growth rates will continue. Otherwise, why would they pay so much for these stocks? Meanwhile, value stocks are cheap for what seems like a good a reason—the market believes their poor past growth rates will continue as well.

But does this really happen? Do cheap stocks have poor realized future earnings growth and do expensive stocks have strong realized future earnings growth? This is an empirical question that can be tested with an experiment. Do growth firms continue to grow faster, on average, or is there a systematic flaw in market expectations?

In Figure 2.6, we look at what happens to earnings growth over the next five years. Specifically, did the value stocks continue to exhibit terrible earnings growth as predicted? Did growth stocks maintain their terrific earnings growth?

No, they did not. The chart is evidence of systematically poor poker playing. The realized earnings growth (dark bars) systematically reverts to the average growth rate across the universe. Value stocks outperform earnings growth expectations and growth stocks underperform their expectations, systematically. Take a moment to study this profound observation. This unexpected deviation from expectations leads to price movements that are favorable for cheap “value” stocks, and unfavorable for expensive “growth” stocks. This deviation explains, at least in part, why expensive stock investors underperform, cheap stock investors outperform, and passive investors receive something in between.

To summarize: Markets, on average, throw value stocks under the bus and clamor for growth stocks. From a poker playing perspective, buying growth stocks and selling value stocks is an example of a systematically poor strategy. Assuming that a great hand from the last round equals a winning hand in the next round is a losing approach. But what are the best poker players doing about this value anomaly situation, and can these poker players easily exploit the poor poker players?

Next Step: Identify the Actions of the Best Players

It is unlikely that we will ever be the smartest investors in the world. For example, George Soros, Julian Robertson, Leon Cooperman, and Paul Tudor Jones will always be smarter than we are. But if we aren't going to be the best player at the investing table, how can we win against these high-powered investors? We can win by finding those market opportunities where the smartest investors are reluctant to participate. But why would a smart investor not want to participate in a straightforward way to beat the market, such as through value investing?

As mentioned previously, smart investors often get endowed with large amounts of capital from a large group of diverse investors (again, think George Soros, Julian Robertson, Paul Tudor Jones, but also large institutions such as BlackRock, Fidelity, and so forth). This makes sense on many levels—investors want to give their money to smart people. The challenge is that the really smart investors are often managing money on behalf of investors that suffer from behavioral biases (System 1 thinkers). Shleifer and Vishny highlight, and the Ken Heebner example confirms, that many smart market participants are hamstrung by the short-term performance measures imposed on them by their investors. “How did you perform against the benchmark this quarter? What do your results look like year to date? What macroeconomic trends are you exploiting this month?” All of these questions are commonplace in the market. The threat of being fired and replaced with a passive portfolio of Vanguard funds is an implied threat. When job security and client expectations trump long-term value creation, funny things happen.

A remarkable paper by Markus Brunnermeier and Stefan Nagel, “Hedge Funds and the Technology Bubble,” highlights the warped incentives faced by the smartest investors who deal with other people's money.¹⁸ Contrary to all textbook teachings related to efficient price formation, the smart money sometimes can be incentivized to enhance mispricing, not trade against it! Brunnermeier and Nagel find that many hedge fund managers didn't try to capitalize on the mispricing between value and growth stocks in the Internet Bubble of the late 1990s—they actually bought growth stocks and sold value stocks. This action enabled them to more closely track the index—for a time. Meanwhile, the poor hedge funds that stuck to their value investing guns, for example, Julian Robertson of Tiger Funds, ended up with no assets under management and a busted business model.

But Julian Robertson wasn't the only famous value investor to lose his proverbial shirt during the 1994 to 1999 time period. Around this time, Barron's famously stated the following regarding Warren Buffett's relative performance:¹⁹

Barron's observation was, in many respects, fully warranted. Value investors as a group were destroyed by the market in the late 1990s. Generic value investing (shown in Figure 2.7) underperformed the broader market by a large margin for six long years!

Obviously, being a value investor requires a patience and faith that few investors possess. In theory, value investing is easy—buy and hold cheap stocks for the long haul—but in practice, true value investing is almost impossible.

Using Ken French's data, we examined just how painful it was to be a value investor in the late 1990s. We examine the returns from January 1, 1994, to December 31, 1999, for a value portfolio (high book-to-market decile, value-weighted portfolio returns), a growth portfolio (low book-to-market decile, value-weighted portfolio returns), the S&P 500 total return index (SP500), and the Russell 2000 total return index (R2K), a small-cap index. Results are shown in Table 2.2. All returns are total returns and include the reinvestment of distributions (e.g., dividends). Results are gross of fees.

	Value	Growth	SP500	R2K
CAGR	18.35%	27.71%	23.84%	13.39%
Standard Deviation	11.79%	16.53%	13.63%	16.96%
Downside Deviation	7.59%	11.25%	10.50%	14.27%
Sharpe Ratio	1.09	1.28	1.30	0.55
Sortino Ratio (MAR 5%)	1.66	1.87	1.67	0.64
Worst Drawdown	–11.58%	–16.33%	–15.18%	–29.78%
Worst Month Return	–8.62%	–14.92%	–14.31%	–19.42%
Best Month Return	8.05%	10.69%	8.04%	11.32%
Profitable Months	68.06%	70.83%	73.61%	66.67%

The returns to the value portfolio were not bad on an absolute basis, but on a relative basis, value was horrific. Looking at the annual returns (shown in Table 2.3), value investing lost almost every year to a simple passive market allocation!

	Value	Growth	SP500	R2K
1994	–2.83%	2.53%	1.35%	–1.82%
1995	36.47%	35.47%	37.64%	28.45%
1996	14.22%	23.20%	23.23%	16.49%
1997	32.52%	31.15%	33.60%	22.36%
1998	29.75%	44.23%	29.32%	–2.55%
1999	5.45%	33.90%	21.35%	21.26%

A plain-vanilla index fund (SP500) outperforms value five out of six years in a row, sometimes by double-digit figures! To simulate what these value managers went through, ask yourself this question:

For 99.9 percent of investors, that answer would be a resounding, “Yes!” (and giving someone a six-year trial period is probably out of the question to begin with). Most—if not all—professional asset managers would be fired, given this underperformance. Asset managers know this evidence intuitively, and internalize the results by avoiding purist value investing endeavors that could make them look like fools in the short run.

After viewing the six-year value investing pain train, we can identify two key takeaways:

1. For a long-term investor, a six-year stretch of pain is a truly great thing. Why? Because this will limit competition from the best pokers players, for whom career risks trump performance considerations, and the weak hands will be shaken out of the competition.
2. Sustainable active investing requires special investors. It requires that investors be disciplined, have a long-term horizon, and be indifferent to short-term relative performance. These unique investors are what we had previously labeled sustainable investors in Figure 2.4.

Now, suspend reality for a moment and let's imagine that an active value manager had clients that didn't run for the exits in 1999. What would their hypothetical returns look like over the long run? As one can see in Table 2.4, value quickly recovers and handily outperforms over the entire time period thereafter. Table 2.4 shows the returns to the same portfolios from January 1, 2000, to December 31, 2014, the 15 years following the six-year period of underperformance.

	Value	Growth	SP500	R2K
CAGR	9.12%	2.75%	4.45%	7.38%
Standard Deviation	24.05%	16.90%	15.22%	20.42%
Downside Deviation	17.73%	12.50%	11.42%	13.77%
Sharpe Ratio	0.41	0.14	0.24	0.36
Sortino Ratio (MAR 5%)	0.37	–0.07	0.05	0.31
Worst Drawdown	–64.47%	–58.21%	–50.21%	–52.89%
Worst Month Return	–28.07%	–16.13%	–16.70%	–20.80%
Best Month Return	36.64%	11.21%	10.93%	16.51%
Profitable Months	58.89%	56.67%	60.56%	58.89%

Sticking with the value strategy, although painful, was richly rewarded with almost a 5 percent edge—per year—over the market benchmark (S&P 500) from 2000 to 2014.

Over the entire cycle, patient and disciplined investors were rewarded. Table 2.5 shows the results over the entire time period, measured from January 1, 1994, to December 31, 2014.

	Value	Growth	SP500	R2K
CAGR	11.68%	9.33%	9.65%	9.06%
Standard Deviation	21.27%	17.00%	14.92%	19.48%
Downside Deviation	16.23%	12.25%	11.19%	13.97%
Sharpe Ratio	0.50	0.45	0.51	0.41
Sortino Ratio (MAR 5%)	0.51	0.44	0.48	0.40
Worst Drawdown	–64.47%	–58.21%	–50.21%	–52.89%
Worst Month Return	–28.07%	–16.13%	–16.70%	–20.80%
Best Month Return	36.64%	11.21%	10.93%	16.51%
Profitable Months	61.51%	60.71%	64.29%	61.11%

What's the bottom line? For a long-term investor, value investing was the optimal decision relative to growth investing, but for many of the smartest asset managers in the world, including the great Julian Robertson, value investing was simply not feasible as a business model. These professionals were often forced via the threat of investor redemptions to “diworsify” their portfolios with overpriced growth stocks during the Internet Bubble. They needed to keep up with the market and did so by doing what everyone else was doing. This decision helped them keep their jobs, but prevented their investors from maximizing their chances for success, even if some truly did have a long-horizon, and discipline.

Putting It All Together

We've used value and growth investing as a laboratory to highlight how the sustainable active investing framework can identify long-term winning strategies. Value investing fits nicely in this paradigm, but has serious warts, notably stretches of horrendous underperformance. The lesson from value investing is that successful active investing is simple, but not easy. If active investing were easy, everyone would do it, and if everyone were doing it, it probably would not generate outsized risk-adjusted returns over the long haul.

In summary, our long-term performance equation from Figure 2.4 highlights two required elements for sustainable performance:

1. The sustainable process exploits systematic investor expectation errors.
2. The sustainable investor has a long horizon and a willingness to be different.

These two pieces of the puzzle map back to the classic lessons of poker:

1. Identify the worst poker player at the table.
2. Identify the best poker players at the table.

And these classic lessons map into the two pillars of behavioral finance:

1. Understand behavioral bias and how investors form expectations.
2. Understand market frictions and how they affect market participants.

So the next time you hear a market participant suggest that one strategy is better than another strategy, simply ask two basic questions: (1) Why are the securities selected by this process mispriced? and (2) Why aren't other smart investors already exploiting the mispricing opportunity? Without solid answers to both questions, it is unlikely that the investment process is sustainable.

But Can We Identify a Better Diversifier?

As outlined above, investors and professional fund managers appreciate the benefits of including growth in a portfolio—especially during the period under discussion—because value and growth had relatively low correlations and thus created a portfolio with less benchmark drift and manageable volatility. However, the inclusion of growth, while providing portfolio diversification benefits, has costs in the form of lower expected portfolio returns. Growth investing is not a sustainable active strategy. In fact, it is just the opposite—a sustainably poor strategy. But what is an investor to do? Ideally, one could capture the diversification benefits of a growth portfolio, but accomplish the diversification benefits with an active stock selection methodology that had characteristics that were more in line with the sustainable active framework.

Fortunately, there is a potential solution to this problem: momentum investing. In the early 1990s, academics such as Narasimhan Jegadeesh and Sheridan Titman, in their 1993 paper “Returns to Buying Winners and Selling Losers: Implications for Market Efficiency,” began to refocus on the old concept of momentum, which refers to a general class of strategies in which past returns can predict future returns.²⁰ That is, if a stock has performed relatively well over the past year, it will continue to perform relatively well in the future. Researchers have done follow-on studies that find the momentum effect persists even when controlling for company size and value factors. And the effect appears to hold over a 200-year time sample,²¹ and across multiple asset classes, such as commodities, currencies, and even bonds.²² Moreover, researchers find that momentum is relatively uncorrelated with value, thus providing diversification benefits. In short, it appears the evidence for momentum is pervasive and provides similar diversification benefits to growth investing.

And while momentum investment strategies are well established in the academic literature, these strategies are not commonly used in actively managed funds, especially when compared with the large number of “growth” funds found in the market. In fact, the immediate gut reaction of most people to “momentum,” is that momentum investing IS growth investing. Unfortunately, this reaction reflects a misconception in the market. Momentum and growth, while sometimes related, are certainly not the same. Moreover, we believe that momentum, unlike growth, fits nicely in the sustainable active framework, thus making it a much better diversifier alongside value, which is another sustainable strategy. The goal of the next chapter is to explain why momentum investing, which is purely focused on prices, is a better alternative to growth investing, which considers both fundamentals and prices. Our mission is to convince the reader that the evidence supports a move to a new style-box paradigm (Figure 2.9) that replaces “growth” with “momentum.”