Factor-Based Investing: The Long-Term Evidence · actor investing and smart beta strate-gies are in...

THE JOURNAL OF PORTFOLIO MANAGEMENT 15SPECIAL ISSUE 2017

ELROY DIMSON

is a professor of finance and chairman of the Newton Centre for Endowment Asset Management at Cambridge Judge Business School in Cambridge, [email protected]

PAUL MARSH

is an emeritus professor of finance at London Business School in London, [email protected]

MIKE STAUNTON

is director of the London Share Price Database at London Business School in London, [email protected]

Factor-Based Investing: The Long-Term EvidenceELROY DIMSON, PAUL MARSH, AND MIKE STAUNTON

Factor investing and smart beta strate-gies are in vogue. A recent survey of major investors reports that almost three-quarters of asset owners are

already using or are actively evaluating smart beta (FTSE Russell [2016]). Of those with an allocation to smart beta, nearly two-thirds are evaluating additional allocations, and the proportion of asset owners using at least five smart beta indexes has risen tenfold, from 2% in 2014 to over 20% in 2016. These market participants, with over USD 2 trillion in assets, include corporations, governments, pension plans and nonprofit organizations, and they have adopted factor investing as an integral part of their strategy.

Exchange-traded funds (ETFs) and exchange-traded products (ETPs) have opened up further opportunities for investors to target asset exposures selectively. By the end of 2016, there were over 6,000 ETFs and ETPs, with over 12,000 listings and assets totaling USD 3.5 trillion; see Fuhr [2017]. There were over 1,000 smart beta equity products, with over 2,000 listings and assets totaling over USD 0.5 trillion. There were 145 smart beta equity providers in 32 dif-ferent countries. Smart beta investing seeks to harvest the long-run factor premiums high-lighted by academic researchers. Factors are the security-related characteristics that give rise to common patterns of return among subsets of listed securities. While industry

and sector membership have long been a part of how we categorize investments, our focus here is on attributes that go beyond industry membership.

To identify factors, researchers typically construct long–short portfolios. These port-folios are long the preferred exposure and short the unwanted exposure. In the equity market, for example, an income factor portfolio would contain higher-dividend yield stocks accompanied by a short position in lower-yielding stocks. It is far easier to buy stocks you do not own than to sell stocks you do not own. So the long side of a factor portfolio is usually easy to acquire, whereas the short side can be challenging. Long–short strategies are therefore relatively expensive—on occasion impossible—to construct, and they can certainly be diff icult to scale up. “Pure play” long–short strategies are some-times called “style strategies.”

It should be no surprise that the growth in smart beta ETFs and ETPs is in long-only portfolios that are tilted (sometimes rather modestly) toward or away from particular factor exposures. These smart beta strategies are long-only, rules-based portfolios that are relatively inexpensive to run, have high capacity, and yet demonstrate a tilt toward or away from specific attributes.

What are the smart beta strategies that researchers have highlighted? Fama and French [1993, 2012, 2015a] identify four

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16 FACTOR-BASED INVESTING: THE LONG-TERM EVIDENCE SPECIAL ISSUE 2017

factors in addition to the market: size, value, profitability, and investment; Black [1972] and Frazzini and Pedersen [2014] identify low risk; and Jegadeesh and Titman [1993] and Carhart [1997] identify momentum. Asness et al. [2015] argue that there are four classic style premiums; namely value, momentum, income (or “carry”), and low-volatility (or “defensive”) investing. Ang, Hogan, and Shores [2016] focus on size, value, momentum, volatility, and profitability.

In all, researchers have identif ied at least 316 factors, of which Harvey, Liu, and Zhu [2016] point out that nearly all are unlikely to be robust in inde-pendent testing. Novy-Marx and Velikov [2015] and Green, Hand, and Zhang [2017] express complementary doubts about the prospective profits from exploiting fac-tors that appear promising on an in-sample basis. The problem of apparently significant in-sample results being nonrobust in out-of-sample (OOS) tests has been dis-cussed for more than a quarter of a century; see, for example, Dimson and Marsh [1990] and Markowitz and Xu [1994]. But there is no substitute for genuine OOS testing. Harvey [2017] notes the impracticality of waiting for additional data in order to test a model’s OOS reliability—not to mention the understandable impatience of practitioners.

OUT-OF-SAMPLE EVIDENCE

The only reliable tests involve examining data for different assets and countries and, especially, for different sample periods. That is the objective of this study. By looking back in time and across countries as well as reporting recent evidence, we aim to answer the question, Does this pattern persist?

An investor’s choice of holdings, sector weights, and geographical exposure has an obvious impact on portfolio returns. But investment performance is also inf luenced by whether a portfolio leans toward small or large companies, value or growth stocks, higher- or lower-yielding securities, momentum- or reversal-based strategies, or defensive or aggressive risk exposures. Asset managers and benchmark providers have recently emphasized these factors. However, size, value, income, momentum, and volatility are far from new phenomena: In fact, all f ive were described three decades ago in Stock Market Anomalies (Dimson [1988]). Since they are among the longest-established and best-documented regularities in the stock market, we can study them,

on an OOS basis, in two ways; first, examining recent data, and second, evaluating truly long-term data.

Although the interval is far too brief to draw conclusions about style premiums, last year’s factor returns are informative. As we will see, in 2016, the momentum factor return was profoundly negative in both the U.S. (−22%) and U.K. (−18%), the income premium was strongly positive and identical in both countries (+15%), and the value premium was similar in the U.S. (+17%) and U.K. (+20%). These premiums illustrate the large absolute magnitude of factor returns. Countries can also have quite divergent experiences. In 2016, the low-volatility factor return was close to neutral in the U.S. (−2%) but was very negative in the U.K. (−21%), and the size premium was positive in the U.S. (+10%) but negative in the U.K. (−5%). The returns to factor exposures vary across risk factors, they can be quite divergent, they differ between countries, and they f luctuate over time. These factors matter a great deal because small companies will continue to per-form differently from large stocks—even if they fail on average to outperform—and similarly, value stocks, high yielders, and past winners will continue to show dif-ferent performance characteristics from growth stocks, low yielders, and past losers, regardless of whether they generate a premium.

Almost all investors are knowingly or unknow-ingly exposed to factors such as size, value, yield, momentum, and risk. It is important, therefore, to understand these exposures when developing an invest-ment strategy or when evaluating a fund manager’s per-formance. Furthermore, a factor that is ranked high in performance in a particular year may remain high, may end up in the middle, or may slip to low in the fol-lowing year. Exhibit 1 lists, in addition to 2016, each year’s factor returns since the financial crisis, ranked from highest to lowest. Since the global financial crisis, the ranking of factor returns has not been stable, and earlier years (not shown here) are similar. Because of the inherent unpredictability of risk premiums, perceptive investors diversify their portfolios across risk exposures.

A frustrating feature of factor risk premiums is that they may simply be transient anomalies in stock market behavior. When that is the case, no sooner have they been identified than they cease to work. Meanwhile, with the benefit of over a century of financial market history, we can try to discern whether there are enduring regularities in stock price behavior, or whether there are


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patterns that ref lect chance events or circumstances that are episodic and cannot be expected to recur. In the sections that follow, we discuss what we can learn from up to 117 years of stock market history, and draw some conclusions on the permanence or transience of factor premiums over the course of such history.

The remainder of this article is organized as follows. In the next section we focus on what was once cited as the stock market’s leading anomaly: the size effect. We trace the size effect since discovery and present evidence over the longest available periods and across multiple markets. We then study the impact of value and growth, and document the tendency of companies selling at a low stock price, relative to fun-damentals, to perform differently and, in the long run better, than companies selling at a high price relative to fundamentals. Relatedly, we document the performance of high- and low-yielding stocks, and go on to report on what happens when stocks are selected according to both size and value. The capstone to our long-term study is an assessment of the performance of momentum and

low-volatility strategies, followed by a summary of other factors that have attracted attention from smart beta researchers and practitioners. After the concluding sec-tion of this article, we provide an extensive reference list.

THE SIZE EFFECT

The size effect f irst came to prominence in the U.S., where Banz [1981] showed that the smallest com-panies quoted on the New York Stock Exchange had provided the highest long-term returns. These findings were subsequently replicated in many other countries, with the longest study being for the U.K., with a com-prehensive history that we extended back to 1955, based on our article Dimson and Marsh [1986], and which has been maintained continuously since then, most recently in Dimson, Evans, and Marsh [2017]. Banz found that there was a particularly substantial premium in returns when the smallest and largest 50 NYSE stocks were compared, with a return advantage to the smaller stocks of one percentage point per month.

E X H I B I T 1Post-Crisis Equity Factor Return Premiums: U.S. and U.K.

Sources: Authors’ data (U.K. premiums and U.S. momentum); French [2017] (other U.S. data).


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The Center for Research in Security Prices (CRSP) at the University of Chicago Booth School of Business provides a long-term history of size-ranked stock indexes running from 1926 (which precedes Banz’s sample period) to the present time. In Exhibit 2, Panel A, we use this as a guide to the size effect in the U.S. The exhibit shows the long-term performance since 1926 of U.S. large-cap, small-cap, and micro-cap stocks. Large caps are defined as the constituents of CRSP NYSE deciles 1–5, small caps as CRSP deciles 6–8, and micro caps as deciles 9–10. While a dollar invested in larger companies, with dividends rein-vested, grew in value to USD 4,690, a similar investment in small caps gave a terminal value more than seven times greater, at USD 33,879. Micro-cap stocks did best of all, with an end-2016 value of USD 53,263. The returns on large-cap stocks were an annualized 9.7%, while small- and micro-cap stocks achieved 12.1% and 12.7%, respectively.

The relative progress of small caps, however, was not consistent and steady, and there were prolonged

intervals of underperformance. Small caps initially per-formed poorly, especially in the Great Depression, and did not catch up with large caps until the early 1940s. By 1975, although micro caps were ahead, small caps were still only marginally beating large caps. During 1975–1983, small caps raced ahead. If this period were omitted, Siegel [2014] notes that large caps would have beaten small caps from 1926 to the late 1990s. As we report below, this patchiness of small-stock returns is also apparent over the last two decades. Yet it may well have been the excellent performance of small caps from 1975 that attracted Banz’s attention and persuaded him to research the size effect. The publication and dissemi-nation of his work led to considerable interest in small caps among U.S. investors, which was helped by their strong outperformance starting in 1975. This spurred the launch of many investment vehicles specializing in smaller companies. The honeymoon period lasted for about two years, until the end of 1983, and, during this period, U.S. small caps continued to outperform. Then, as we reported in Dimson and Marsh [1999], U.S.

E X H I B I T 2Performance of Micro-, Small- and Large-Cap Stocks: U.S. 1926–2016 and U.K. 1955–2016

Sources: Dimson, Marsh, and Staunton [2002], and subsequent research. U.S. CRSP capitalization decile returns from Morningstar. U.K. is Numis Smaller Companies Index.


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small caps began to underperform, becoming a victim of Murphy’s Law. Over the period 1984–1997, the small-cap premium turned negative; although, ironically, after we highlighted the demise of the size effect, U.S. small caps performed very well over the first decade of the 21st century in both relative and absolute terms. Our 1999 warning on reversal of factor premiums was followed in the new century by a spate of papers reporting similar f indings for other factor-driven strategies, a topic to which we return later.

For the U.K., the long-term evidence is similar. Our analysis is based on the performance of the Numis Smaller Companies Index (NSCI), which comprises the lowest tenth by value of the U.K. equity market, and the Numis Mid Cap index, which covers the lowest fifth by value, excluding the smallest 5% of the market. These indexes have a 62-year history. Extensive analysis of the index history is contained in Dimson, Evans, and Marsh [2017], and the literature is reviewed in Dimson, Marsh, and Staunton [2002]. In Exhibit 2, Panel B we plot the performance of U.K. small caps and mid caps and draw comparison with our large-cap equity index. The chart also shows our MicroCap™ index, which tracks the bottom 1% by value of the U.K. market, on an ex–investment companies basis.

The graph shows that one pound invested in the U.K. large caps at the start of 1955, with dividends reinvested, would have grown to GBP 1,087 by end-2016, giving an annualized return of 12.0%. The same invest-ment in the Mid Cap would have generated GBP 3,220, while the NSCI would have generated GBP 6,861, well over six times as much as large caps. However, an invest-ment in micro caps would have yielded GBP 27,256, four times as much as from the NSCI, and equivalent to an annualized return of 17.9%. These returns are gross of tax and before transaction costs. While the latter would obviously be higher for small caps and especially for micro caps, it is clear that, over the long run, there has been a marked size premium in the U.K., with the very smallest stocks doing best.

The NSCI went live at the start of 1987 and, despite its impressive back-history, it did not prove to be the road to instant riches. As noted in Dimson and Marsh [1999], soon after the U.S. size premium went into reverse, the same thing happened in the U.K. and virtually all other markets around the world. Much of the 1990s proved to be the era of the large-cap stock, and careful scrutiny of Exhibit 2 reveals that the gap in cumulative

returns narrowed over this period (although not to the point of eroding all the accumulated performance that had built up since 1955). Even in years when small caps underperformed, however, the size effect lived on in the sense that small caps still deviated in their performance from large caps.

With the discovery of the size premium in the U.S. and then the U.K., researchers set out to investigate whether this also occurred in other countries. Dimson [1988], Hawawini and Keim [2000], and Schwert [2003] summarized these studies. It became clear that the size premium was not restricted to the U.S. and the U.K., but had been present in almost every country studied. Furthermore, in most countries, researchers concluded, like Banz, that the size premium could not be explained away by risk. The pervasiveness and magnitude of the small-cap premium meant that it rapidly became rec-ognized as the premier stock market anomaly of its day.

In the periods immediately following those studied by the various researchers, however, the size premium was on average smaller, more often negative, and more volatile than in the earlier research period. To gain a full picture of the entire record, including recent data, we estimate each country’s long-run small-cap pre-mium. We take the results obtained from the various research studies and update them to the present day using commercially available small- and large-cap indexes. Full references to the earlier research studies, and the indexes used to update them, are given in Dimson, Marsh, and Staunton [2004]. Since 2004, the MSCI small- and large-cap indexes have been used to update the size premium in all countries except the U.S. and U.K., where, for consistency, size premiums have been defined as above.

The results of this long-run cross-country compar-ison are shown by the blue bars in Exhibit 3, which plots the average difference between small- and large-cap returns for all 23 countries covered in our research. The period covered is the longest possible for each country, beginning at the start date of the earliest research into that country’s size effect. On average, the period is just over 43 years (520 months), ranging from 17 years for Austria, Portugal, and Norway to 91 years for the U.S. Viewed in an international context, smaller companies have given a modest long-run premium, relative to large companies, averaging 0.32% per month. While this contrasts with the much higher premiums that were reported in the initial research studies, the OOS 21st


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century premiums (compare the bars labeled “Avg”) are actually larger than in the 20th century.

The size premium still has the capacity to surprise. During the 1990s, just when many investors gave up on small caps after a decade of poor relative performance, the size premium reasserted itself. The turquoise bars show the average size premium over the 17 years of the 21st century to date. This may have been a diffi-cult period for equity markets, but was mainly due to the disappointing performance of large caps. Small caps performed well, giving positive returns in almost all developed and emerging markets. The size premium was positive in every country except Norway, and averaged 5.6% per annum. Over this period, the MSCI world small-cap index enjoyed a 5.5% premium over the MSCI world large-cap index.

Despite the record of the last decade, if researchers were for the first time investigating the long-run returns on smaller companies, they would today recognize the small-firm effect (the tendency of small caps to perform differently from large caps) and would note that there had been a modest small-firm premium. But the magni-tude of the premium would command less attention than in the past and would not suggest there was a major “free lunch” from investing in small caps. The size premium would no longer be deemed the premier anomaly.

Nevertheless, small caps continue to perform very differently from large caps, and it is clear that the

small-cap effect (the tendency of small caps to perform differently) lives on, even if the small-cap premium (the tendency for small caps to outperform) has proved lower in practice than in initial discovery. Furthermore, there is no doubt that portfolios tilted toward or away from the market’s average size exposure are taking on significant risk relative to the market as a whole. Prudent long-horizon investors should plan on no more than a normal reward for the risk, illiquidity, and management costs associated with running a portfolio of low-capitalization stocks. However, we can see no case for deliberately underweighting smaller companies.

THE VALUE PREMIUM

In addition to the size effect, there has historically been a relationship between the value or growth orienta-tion of an investment strategy and its long-run perfor-mance. Value stocks sell for relatively low multiples of earnings, book value, or dividends. They may be mature businesses with an unexciting future, or they may have a depressed share price that ref lects or anticipates set-backs. Growth stocks sell for relatively high valuation ratios, ref lecting favorable prospects for the business, and their shares anticipate cash f lows that are expected to grow in the future. There is an extensive literature documenting that, over the long run, value and growth investing have given rise to substantially different records

E X H I B I T 3Small-Firm Premium in 23 Countries, Period to End of 2016, % Per Month

Source: Dimson, Marsh, and Staunton [2004], and subsequent updates using MSCI indexes.


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of long-term performance. A large body of U.S. evidence shows that value stocks have outperformed growth stocks.

Style-based indexes assign stocks to value or growth (or a middle group) based on accounting data and estimated growth rates, usually in conjunction with a measure of company size. FTSE Russell [2017] uses a composite of cash f low yield, earnings yield, and country-relative sales/price ratio. S&P [2015] uses three factors: the ratios of book value, earnings, and sales to price. Wilshire [2010] looks at six factors: projected price-to-earnings ratio, projected earnings growth, price-to-book ratio, dividend yield, trailing revenue growth, and trailing earnings growth. MSCI [2015] uses three factors: book/price, 12-month-forward earnings/price, and dividend yield. Overall, value strategies typically emphasize stocks with a high book-to-price ratio.

Exhibit 4 shows the performance of value versus growth stocks in the U.S. This is based on the Fama–French index series, which is the longest available U.S. history, with a 1926 start date. Fama and French classify stocks into value, middling, and growth, based on their book-to-market ratio, and compute monthly returns

that are compounded over time. The performance of value (high book-to-market) stocks is superior over this long time period, and growth stocks are inferior. For U.S. companies, the graph shows that, over the longest available period (end-1926 to end-2016), the (geometric) difference between the annualized returns on the Fama–French value and growth indexes is 3.3%.

After the U.S., the country with the longest recorded history of the value effect is the U.K. Dimson, Nagel and Quigley [2003] examined the U.K. value effect since 1955, and we have updated their results to the end of 2016 using their methodology. Value is again measured by the ratio of the book value of equity to the market value of equity. U.K. stocks are sorted into growth (the 40% with the lowest book-to-market ratio), medium, or value (the 40% with the highest book-to-market ratio). The U.K. graph in Exhibit 4 compares the long-run performance of the low and high book-to-market portfolios. A one-pound investment in the growth index in 1955 would have grown in nominal terms to GBP 419 by the end of 2016, an annualized return of 10.3%. The same initial investment allocated to

E X H I B I T 4Performance of High and Low Book-to-Market Stocks: U.S. 1926–2016 and U.K. 1955–2016

Sources: Dimson, Marsh, and Staunton [2002], and subsequent research; French [2017] (U.S. data); Dimson, Nagel, and Quigley [2003] (U.K. data).


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the value index would have generated GBP 9,173, more than 21 times as much, and equivalent to an annualized return of 16.0%. Value investing paid off handsomely.

Value investing was a winning strategy in the period covered by the major research studies, but as in other areas of investing, the subsequent outcome was for several years the opposite of what history had led investors to expect. In particular, the 1990s was mostly the era of growth stocks, and value strategies fared poorly. After March 2000, value investing came back into its own, with value stocks performing especially well in the early years of the 21st century, but with growth stocks again in the ascendant from 2007 onward.

To provide a global perspective, we document the value premium in other stock markets, focusing on countries for which we have long-term stock market data. We update the estimates reported by Dimson, Marsh, and Staunton [2004], who in turn draw on Capaul, Rowley, and Sharpe [1993], Chan, Hamao, and Lakonishok [1991], Fama and French [1998], and Rosenberg, Reid, and Lanstein [1985]. In particular, we compute the annualized value premium for each country as the geometric difference between the MSCI Value and Growth indexes from inception (1975, in most coun-tries) to date. MSCI constructs these indexes using eight historical and forward-looking fundamental variables for each security. They define value using a combina-tion of book value/price, earnings/price, and dividend yield, while they define growth based on a combina-tion of variables measuring short- and long-term growth in EPS and sales per share. They place each security into either the Value or Growth Indexes, or partially allocate it to both. The blue bars in Exhibit 5 show the value premium for 23 countries over the 42 years from 1975 to 2016 (seven MSCI Value and Growth indexes start later: Finland and New Zealand in 1988, Ireland in 1991, China and Portugal in 1995, and Russia and South Africa in 1997). Taking a global and long-term per-spective, value mostly beat growth investing. The value premium was positive in 20 countries, negative in three. The premium on the world index was 2.1% per year.

The superior returns from value stocks arrived erratically, however. Dimson, Nagel, and Quigley [2003] report considerable year-to-year variation. Fur-thermore, as noted above, value tended to perform less well in the periods after researchers first documented the effect. In particular, the 1990s was a poor decade for value stocks. After the tech crash, value stocks returned

to favor. The turquoise bars in the chart show that, since 2000, there have been positive value premiums in all but four countries, and an annualized value premium on the world index of 2.5%. However, no investment style delivers premiums with certainty, and growth stocks outperformed in a majority of countries from 2007 to 2015, with value reasserting itself in 2016. There is still much controversy over the source of the value premium. Dimson, Marsh, and Staunton [2004] review some of the disputes about the robustness of the premium, and the question as to whether it relates to behavioral factors or is simply a reward for greater investment risk, an issue to which we return below. The central issue, of course, is whether value will continue to triumph over the long run and, if so, whether its superiority more than com-pensates for any higher investment risk.

INCOME AND YIELD

Since the 1970s, a number of U.S. researchers have documented a marked historical return premium from U.S. stocks with an above-average dividend yield. The most up-to-date analysis is by Kenneth French of Dartmouth University. Exhibit 6 shows the most recent data, covering the period since June 1927. Stocks are ranked at the start of each year by their dividend yield, and divided up into the highest- and lowest-yielding 30% of dividend-paying companies, the middle 40%, and stocks that pay no dividends. Over this 89½-year period, the annualized return from the nondividend-paying stocks was 8.5%, while low-yield stocks returned 9.0%, and high yielders gave 11.3%. However, the lon-gest study of the yield effect is our 117-year research on the U.K. equity market. Prior to the start of each year, we rank the 100 largest U.K. stocks by their dividend yields, and divide them equally into higher-yielding and lower-yielding stocks. We calculate the capitalization-weighted returns on these two portfolios over the fol-lowing year, and then repeat this procedure each year. Exhibit 6 also shows that an investment of GBP 1 in the low-yield strategy at the start of 1900 would have grown to GBP 6,810 by the end of 2016, an annualized return of 7.8%. However, the same initial investment allocated to high-yield stocks would have generated GBP 158,727 (23 times greater), equivalent to a return of 10.8% per year.

Exhibit 7 shows that the yield effect has been evident in most countries examined. The data again


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E X H I B I T 5Annualized Value Premium in 23 Countries, 1975–2016, % Per Year

Source: MSCI Value and Growth indexes.

E X H I B I T 6Performance of Low- and High-Yield Stocks: U.S. 1927–2016 and U.K. 1900–2016

Sources: Dimson, Marsh, and Staunton [2002] and subsequent research; Top 100 stocks, (U.K. data); French [2017] (U.S. data).


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comes from Ken French and covers 21 markets—all the countries for which we have stock market data starting in 1900, except China, Portugal, Russia, and South Africa, but including Hong Kong and Singapore. For most countries, the period covered runs from 1975 to 2016. The premium is based on the highest- and lowest-yielding 30% of dividend-paying stocks. For a few countries, the data starts after 1975, whereas for the U.S. and U.K. we take the yield premiums from the much longer studies mentioned previously. The bars display the annualized yield premium, defined as the geometric difference between the returns on high- and low yielders. The blue bars show the premiums over the longest period available for each country. High-yielding stocks outperformed low yielders in every country except New Zealand and Ireland, which are both small markets. Across all 21 countries, the average premium was a striking 3.9% per year. The turquoise bars in the chart show the yield premium over the f irst 17 years of the 21st century up to the end of 2016. The premium was positive in 19 of the 21 countries, and averaged 6.2%, far above the level of the longer-term period reported above. This period embraces the dot-com bust, when technology, media and telecommunications stocks (which were mostly zero- or low-yielding securities) tumbled from their dizzy heights as investors re-engaged in stocks with

strong fundamentals, including dividends. It also spans the credit and financial crisis. This explains the nega-tive premiums for Ireland and Belgium, both of which were heavily exposed toward high-yielding banks that subsequently performed poorly.

The yield premium is now widely viewed as a manifestation of the value effect that we discussed in the previous section, since high yielders are stocks that sell for low multiples of a fundamental variable, namely dividends. In the context of yield, value stocks or high yielders may be mature businesses or else dividend payers with a depressed share price that ref lects recent or anticipated setbacks. Growth stocks, in contrast, often pay low or no dividends since the companies wish to reinvest in future growth. They sell on relatively high valuation ratios because their stock prices anticipate these growing cash f lows (and dividends).

Why have high yielders outperformed low and zero yielders? There are four possibilities. First, it may simply be by chance and hence unlikely to recur. How-ever, this is hard to sustain, as while there have been lengthy periods when the effect fails to hold, it has nevertheless proved remarkably resilient both over the long run and across countries. A second possibility is that we are observing a tax effect, since many countries’ tax systems have favored capital gains, perhaps causing growth stocks to sell at a premium. The impact of tax is

E X H I B I T 7The Yield Premium around the World, 1975–2016, % Per Year

Sources: Authors’ data, except for French [2017] (U.K. data).


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controversial, but tax alone seems unlikely to explain the large premium. Furthermore, in the U.K., there was a yield premium pre-1914, when income tax was just 6%. Also, if tax were the major factor, alternative definitions of value and growth stocks would work far less well than dividend yield as an indicator of high or low subsequent performance. We have analyzed the most commonly used alternative measure, the book-to-market ratio (see the previous section), based on the same markets and time periods, and found that it performs almost as well as yield.

A third possibility is that investors become enthused about companies with good prospects, and bid the prices up to unrealistic levels, so growth stocks sell at a premium to fundamental value. Evidence for this is provided in Arnott, Li, and Sherrerd [2009], wherein the authors analyzed the constituents of the S&P 500 in the mid-1950s, comparing the stock prices at the time with what they termed “clairvoyance value.” This was the price investors should have paid if they had then had perfect foresight about all future dividends and distribu-tions. Arnott classified growth stocks as those selling at a premium, i.e., on a lower dividend yield or at higher price/earnings, price/book or price/sales. Arnott and his colleagues found that the market had correctly identified the growth stocks, in that they did indeed reveal superior future growth. However, they also concluded that inves-tors had overpaid for this growth by up to twice as much as was subsequently justified by the actual dividends and distributions to shareholders.

The final possibility is that the outperformance of value stocks is a reward for risk. This could explain Arnott’s findings, if the discount rates used to compute “clairvoyance value” had failed to cater adequately for risk differences. However, many investors believe the opposite, namely that high yielders are less risky. Perhaps this ref lects the view that a bird in the hand (a dividend banked) is more secure than two in the bush (future returns). If so, this ref lects confusion. If investors wish to maintain their desired weighting in stocks, they must reinvest their dividend, which then re-exposes them to equity risk. Although cash is safer than stocks, investors should have factored this in when establishing their desired exposure to equities.

Investors may also perceive high-yielding stocks to be lower risk because of sector membership. Utilities tend to have higher yields and are typically lower risk. However, investors may well once have thought the

same about bank shares. Furthermore, many high-yielding stocks are “involuntary” high yielders. They have acquired their high yield because their stock price has fallen. They may be struggling or distressed, and their future dividend may be uncertain.

In Dimson, Marsh, and Staunton [2011], we examined the risks from investing in higher, lower, and zero yielders, and an index investment in each of the countries covered in Exhibit 7. We found that higher-yield strategies were less volatile than lower- and zero-yield portfolios. Furthermore, the average standard deviation of returns from investing in high yielders was only marginally above that from an index fund, despite the latter being far more diversif ied. We also looked at beta, which measures systematic risk, or the contribution to the risk of a diversif ied portfolio. We found that the higher-yield strategy had a lower average beta (0.89) than the beta of the market (1.0) and the beta of the lower- and zero-yield strate-gies. F inally, we examined the historical Sharpe, or reward to variability, ratios. The Sharpe ratio of the higher-yield strategy was 0.42, almost twice that of the lower-yield strategy, and almost three times that of the zero-yield strategy. It was also appreciably higher than the average Sharpe ratio from investing in the country index funds. It is thus hard to explain the superior performance of higher yielders in terms of risk, when, on conventional measures, they have lower volatility and betas.

VALUE AND SIZE

There is evidence in both the U.S. and the U.K. that the value premium is more substantial among small companies than among large companies. The most authoritative studies are the sequence of papers by Fama and French, beginning with their 1993 article. Their research procedure involves identifying value stocks as those that are among the top 30% of book-to-market ratios, and as growth stocks the 30% with the lowest book/market. They also classify those stocks that are in the top 50% by market capitalization as big, and the other 50% as small. Based on these two independent rankings, Fama–French then intersect these four groups to create four portfolios with a value or growth orien-tation and with a large or small company size. They then compute returns for these four portfolios on a capitalization-weighted basis.


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We show the long-term performance in the U.S. of small and large stocks and of value and growth stocks in Exhibit 8, which displays the cumulative performance of these portfolios. Big growth stocks generate an annualized return of 9.6%, whereas small growth stocks generate an annualized return of 8.7% per year. These returns are rather close: The size tilt of the indexes has little impact. Compared with growth stocks, value stocks do better, with big value companies having an annual-ized return of 12.2%, and small value stocks having a return that is some three percentage points higher still, at 15.1% per year.

With our lengthy U.K. database, we can also ana-lyze value and size effects in the U.K. stock market. We measure value by the ratio of the book value of equity to the market value of equity, and sort stocks into growth (the 40% with the lowest book-to-market ratio), medium, or value (the 40% with the highest book-to-market ratio). We rank stocks by market capitalization and sort them into big (above the 70th size percentile) or small (below the 70th percentile). We form portfolios

from the intersection of these two sorts and calculate their 12-month returns. The methodology is very similar to the Fama–French approach, and is explained in Dimson, Nagel, and Quigley [2003]. Exhibit 8 reports the performance of the four U.K. equity port-folios classif ied by whether they have a small or big market capitalization, and by whether they are classified as value or growth. The top line confirms that there has been a pronounced small-cap, value effect in which small value stocks excelled. As can be seen on the right of the chart, an end-June 1955 investment of one pound in small value companies, with income reinvested, would have been worth GBP 35,818 by the end of 2016, an annualized performance of 18.6%.

Value stocks did better than growth stocks. Consequently, after small value stocks, big value stocks were the next-best performers. However, after 61½ years, an investment in these stocks had grown in value to less than one-sixth of the small value portfolio. The 1990s technology bubble f leetingly drove small growth stocks to be excellent performers compared with small

E X H I B I T 8Performance of Size- and Value-Based Portfolios: U.S. 1926–2016 and U.K. 1955–2016

Sources: Dimson, Marsh, and Staunton [2002] and subsequent research. French [2017] (U.S. data).


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value stocks, but this closed the long-term performance gap by only a bit. These companies collapsed after 2000, while small value reverted to its long-term pattern of striking outperformance. Growth stocks were a long-term disappointment, whether the companies were small or big. An investment of one pound in small growth companies was worth GBP 1,812 after 61½ years—far beneath the GBP 35,818 of a similar investment in small value companies. Yet big growth companies were the biggest disappointment of all. With income reinvested, an investment in big growth ended up with a f inal value of less than 1.25% of the final value of the small value portfolio.

MOMENTUM

In well-functioning markets, it should not be pos-sible to earn consistently superior returns from the naïve strategy of buying past winners and selling losers. From the earliest days of research into market efficiency, it had appeared that stock returns were not reliably persistent, at least over short horizons. However, in the late 1980s, Conrad and Kaul [1988] and Lo and MacKinlay [1988] reported trends in weekly stock returns that persist for short intervals, although they were hard to exploit. Then, in a seminal study of U.S. stock returns over the period 1965–1989, Jegadeesh and Titman [1993] found that, over an intermediate time horizon, past winners tend to achieve superior performance and past losers suffer inferior performance. While the short-term returns of individual stocks were unpredictable, returns on momentum portfolios over an intermediate horizon (say up to 12 months) were persistent and of a magnitude that appeared to exceed transaction costs. A strategy of buying stocks that did well over the prior year (win-ners) and selling those that did poorly (losers) typically generated monthly profits of around 1%. These profits were significant within small, medium, and large firms, and across beta-based subsamples, and were attainable because performance was attributed mainly to the long, rather than the short side of the portfolio.

Jegadeesh and Titman’s work sparked off numerous other studies. Rouwenhorst [1998], Chui, Titman, and Wei [2003, 2011], and Griffin, Ji, and Martin [2003] examined non-U.S. markets over the period 1975–1997 and in 40 international markets over intervals ending in 2000, with effects that were more pronounced in developed than in emerging markets. There are also

holdout-period tests that follow the original research period, such as Jegadeesh and Titman [2001]. Studies of momentum, including Dimson, Marsh, and Staunton [2008], use Jegadeesh and Titman’s [1993] method-ology. This involves ranking stocks based on their past returns over (say) the most recent 3, 6, or 12 months. The top-performing stocks are assigned to a “winner” portfolio, and the bottom performers to a “loser” port-folio. Researchers such as Jegadeesh and Titman [1993], Griffin, Ji, and Martin [2003], and Carhart [1997] use different definitions of winners and losers, but typically they are taken to be the top and bottom 10%, 20%, or 30% of stocks. The investment strategy is to buy the winners and short sell the losers, holding this position for (say) 1, 3, 6, or 12 months. To allow time for imple-mentation, and to avoid contamination from the bid–ask bounce and any short-term reversal effects, there is usu-ally a “wait period” between the ranking and holding periods. An R/W/H momentum strategy has a ranking period of R months, a wait period (if any) of W months, and an H-month holding period. Thus a 6/1/6 strategy involves ranking stocks by their returns over six months, waiting for one month, and then buying the winners and shorting the losers over the next six months; the strategy is then repeated. The strategies most frequently used are 6/1/6 and 12/1/1, although many studies show results for a range of alternatives.

To judge the performance of momentum strategies, researchers compute winner-minus-loser (WML) port-folio returns. Typically, the winner and loser portfolio returns are an equal-weighted average of the returns on portfolio constituents. As this can give too much inf lu-ence to micro-size stocks, many studies screen these out or use capitalization weighting instead. Researchers cal-culate WML returns as either the arithmetic or the geo-metric difference between winner and loser returns. The WML performance measure is for a notional long–short portfolio. In the absence of a momentum effect, WML should generate a cumulative return of zero, but that is not what has happened, as can be seen in Exhibit 9, where we see a large difference in cumulative perfor-mance from winner stocks compared with loser stocks. Following a 6/1/6 momentum strategy, an investment in U.S. loser stocks would have appreciated from one dollar in 1926 to USD 3,634 as at the start of 2017—a nominal return of 9.5% per year. In contrast, an equal initial investment over the same period, but allocated to winner stocks, would have appreciated to USD 2.1


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million—an annualized return of 17.5%. The winner portfolio would have been worth 586 times as much as the loser portfolio, equivalent to 7.4% per year.

Profits like this are surprising because investors might be expected to compete to benefit from the WML premium. It would not be necessary for them to run long–short portfolios to profit from an apparent arbi-trage opportunity. It would only be necessary for inves-tors to recognize this pattern among stock prices. Then potential buyers would seek exposure to winners, and they would be willing to pay a little more to acquire these stocks—and in so doing, the winners’ overperfor-mance would be reduced. Similarly, potential sellers of a security ought to appreciate that they should hasten to sell stocks that are drifting down—which would depress the transaction price for these securities to the point at which the losers’ underperformance would be curtailed.

There are always buyers and sellers for every stock that is traded in the WML portfolio. The mystery is the price at which stocks change hands. Relative strength

is not new; it was a researched half a century ago by Alexander [1961] and Levy [1967a,b]. It was not costly to exploit. It did not require creation of a market-neutral fund. For the momentum effect to be arbitraged away, all that is required is use by regular market participants; that is, for buyers and sellers to pay cognizance to rela-tive strength. Moreover, if the strategy was obvious only with hindsight, once it was endorsed by Jegadeesh and Titman, this regularity should have disappeared. Yet, as we will see, momentum has worked OOS as well as in sample.

To investigate the robustness of the momentum effect, we can limit portfolio constituents to relatively liquid stocks (the largest 100 companies), and can study a market (the U.K.) and sample period (117 years) that has not been investigated by other researchers. Exhibit 9 also shows that, for a universe of the Top 100 U.K. stocks as at the start of each year, from 1900 to 2016 winners out-performed losers by 10.2% per year. The graph is based on a momentum strategy that replicates the most-cited

E X H I B I T 9Performance of Momentum Strategy Based Portfolios: U.S. 1926–2016 and U.K. 1900–2016

Sources: U.S. ( following a 6/1/6 strategy) Griffin, Ji, and Martin [2003] to end-2000 updated to end-2016 by authors; U.K. data ( following a 12/1/1 strategy) from authors.


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U.S. literature. For the period 1955–2016, we have a complete dataset, and we find that the WML premium is even larger if (a) the strategy covers the full market and not just the Top 100, (b) portfolios are equally weighted (rather than value weighted as in the Exhibit), and (c) the winner/loser breakpoints are the top and bottom 10% of stocks (rather than 20%, as in the exhibit). We also find that holding periods of 6 months are more profitable than those of 12 months (as in the exhibit).

There are two significant caveats about momentum strategies. First, they are potentially costly to implement because turnover levels are high, especially for strategies that require frequent rebalancing. However, although transaction costs eat into the profits from momentum trading, they by no means eliminate them. The impact of costs on the Top 100, 12/1/1 strategy is discussed more fully in Dimson, Marsh, and Staunton [2008]. The experience of 2009 underlines our second caveat. The returns from a WML strategy are volatile, with the occasional very bad year when markets sharply reverse direction. This happened in 2009, when the dip in the long-run WML series for the U.K. represents an annual loss from the WML strategy of 25.4%. Looking back in time, investors would have been similarly whiplashed at the turning points in 1975, 2000, and 2003, when

annual losses of similar magnitude occurred. These were all strong years for long-only equity performance. The relative performance of a pure momentum man-ager would thus have looked woeful and would have severely tried the patience even of long-term investors. Furthermore, although the losses during 2009 from the capitalization-weighted Top 100, 12/1/1 strategy were large, they were even larger for strategies that admitted more volatile stocks and/or used equal weighting. In the U.K., the losses in 2009 from an equally weighted 6/1/6 strategy spanning the entire market were −46%, while in the U.S., the corresponding loss was −53%.

In Exhibit 10, we ask whether the momentum effect is apparent in other markets around the world, drawing on over four decades of evidence for the 23 countries covered in this study. We extend the study by Griffin, Ji, and Martin [2003], who examine momentum investing in many countries, and who generously provided us with their dataset. They study markets over intervals that start in 1926 (the U.S.), 1975 (10 larger European markets), the mid-1980s (Norway, Spain, and Sweden), the late-1980s to 1990 (Austria, Denmark, Ireland, Portugal, and South Africa), 1994 (China), and other dates (countries not examined here). For the 23 countries, we update and extend Griffin, Ji, and Martin’s database and research,

E X H I B I T 1 0Returns from a 6/1/6 Momentum Strategy in 23 Countries, 1975–2016, % per Month

Sources: Griffin, Ji, and Martin [2003] WML returns to 2000; Dimson, Marsh, and Staunton [2008] WML returns after 2000. All WML returns are 6/1/6 with 20% and 80% breakpoints.


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and we incorporate Russia, which was not in their study. We use their WML estimates up to end-2000. There-after, to end-2016, we compute our own estimates using their methodology. This involves following a 6/1/6 strategy, with a monthly rolling window and equal weighting, using 20/80 breakpoints.

Exhibit 10 shows that, over the period to the end of 2000, Griffin, Ji, and Martin [2003] found that the monthly WML return was positive and quite large for all countries except China, Japan, Portugal, and Sweden, where, except for Portugal, it was close to zero. The momentum pre-mium was substantial over this period, averaging 0.71% per month for the 22 countries in the exhibit that were covered by their study (see bar labeled “Avg”).

After 2000, we f ind that momentum investing was profitable in 21 of the 23 markets, the important exceptions being Japan and the U.S. Unusually for a “holdout” period, we f ind that for the 22 markets shown in Exhibit 10 that were covered in Griff in, Ji and Martin’s original research, the average WML in the 16-year period following their study was actually greater than it was over their research period, namely 0.87% per month. The blue bars show each country’s WML returns over the entire period to date. Exhibit 10 shows that the average WML return per month over this full period was 0.79% if each country is given equal weight. If, instead, we weight by the number of observations for each country, the average WML was slightly lower at 0.76% per month. But both of these figures are above the average WML of 0.71% per month for the original research period. Notwithstanding the poor experi-ence of 2009, and although it can be a costly strategy, momentum trading has generated a disturbingly large abnormal return.

VOLATILITY

From the earliest empirical studies of risk and return, it was apparent that the return from low-beta securities was higher than the classic Capital Asset Pricing Model (CAPM) implied, and that the return from high-beta securities was not as high as the CAPM predicted. The observation made by Black, Jensen, and Scholes (BJS) [1972] was that the Security Market Line—the link between return and beta—was “too f lat,” which suggested potential for outperformance. The BJS finding indicated that investors who could borrow at the risk-free rate would have a higher expected return

if they bought a levered low-beta portfolio than if they purchased a higher-risk portfolio. Is that apparent “free lunch” achievable? That is, can investors borrow at the risk-free rate? The answer is simple. All investors whose asset mix includes risk-free government bills or short-term bonds can “borrow” by reducing their holding of government securities. There are many such inves-tors. Simply by amending their asset mix, they could in effect be borrowing at the same rate as the government. The BJS finding was one of the earliest reported stock market anomalies.

The beta anomaly has been persistent. Frazzini and Pedersen [2014] estimate a betting-against-beta (BAB) factor that involves taking a long position in a portfolio of low-beta assets, leveraged to a beta of 1; plus a short portfolio of high-beta assets, unleveraged to a beta of 1. They find support for the profitability of a BAB strategy in the U.S. and other countries. Asness et al. [2016] dig further into this relationship. But even more remarkable is the low-volatility anomaly. In long-forgotten studies, Friend and Blume [1970] reported a significant inverse relation during the 1960s between volatility and return, and Haugen and Heins [1975] found that U.S. returns starting in 1926 were higher for low-volatility stocks. More recently, U.S. studies such as Ang et al. [2006, 2009] revealed severe underperformance from stocks with high residual risk. Exhibit 11 plots the cumulative U.S. returns estimated by French [2017] for portfolios ranked by the residual variance from the Fama–French three-factor model. The 20% of stocks with the highest risk underperformed dramatically.

Exhibit 11 also plots cumulative U.K. returns computed by us for portfolios ranked by the residual variance from the single-factor model. The sample comprises all companies listed on the London Stock Exchange. Similar to the French [2017] research for the U.S., risk is estimated over the preceding three months of daily returns. U.K. stocks are assigned to the lowest 30%, middle 40%, and top 30% of residual risk. As in French [2017], portfolios are rebalanced monthly, and returns are value-weighted. The exhibit data starts in 1984 because that is the start year for the daily version of the London Share Price Database (LSPD).

Both the U.S. and U.K. studies broadly follow the Ang et al. [2006, 2009] methodology in that they are based on short-term estimates of residual risk. For the U.S., French uses approximately three months (60 trading days) of data; for the U.K., we use exactly


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three months of daily data. In both countries, the strategy reveals substantial underperformance by high-risk stocks. The performance gap compared with less-risky stocks expands over most of the sample period, but with one notable exception. High-risk stocks par-tially closed the gap toward the end of the 1990s. Sadly, the tech boom of that period burst in 2000, and the value of high-risk portfolios collapsed. High-volatility stocks suffered again in the late 2000s in the face of the financial crisis.

The results reported here for the U.S. and U.K. are confirmed in studies of other countries. The find-ings of Ang et al. [2006, 2009] have been subject to a comprehensive set of tests, many of them summarized and critiqued in a recent paper by Hou and Loh [2016]. The phenomena noted by Ang et al. appear to hold up well in a range of markets, although there is not yet agreement on the causes of this risk anomaly. However, volatile stocks tend to have a relatively low market capi-talization. This presents an implementation challenge in that a long–short strategy aiming to take advantage of

the underperformance of risky stocks would be shorting the highest-risk companies. For the U.S. and U.K., low-risk companies represent an average 40% and 54% of the value of the equity market, respectively, whereas high-risk companies represent an average of just 8% of the market in both countries. Bali and Cakici [2008] argue that the significant negative relation reported by Ang et al. [2006] is driven by small-cap stocks and that, if these securities are omitted, the results become statisti-cally insignificant. Fu [2009] draws similar conclusions.

Furthermore, short-term risk measures are esti-mated with considerable error, and the true underlying risk of a stock is not only unobservable, but must also be time varying. Consequently, portfolio turnover for these risk-based strategies can be expected to be high. Meanwhile, the cost of transacting small- and micro-cap securities is large. Like many momentum strategies, the Ang et al. strategy may be impractical for many long-term investors. For this reason, practitioner-researchers such as Blitz and van Vliet [2015] have investigated using longer-term risk measures, and running lower-turnover

E X H I B I T 1 1Performance of Stocks Ranked by Short-Term Residual Risk: U.S. 1963–2016 and U.K. 1984–2016

Sources: French [2017] (U.S. data. Portfolios contain 20% of stocks, and rebalanced monthly based on residual variance measured over preceding 60 days; Authors’ data (U.K. data. Main market stocks are assigned to the lowest 30%, middle 40%, and top 30% of three-month risk). All portfolio returns are value weighted.


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portfolios. They report a number of studies, many by them and their colleagues, that demonstrate superiority from low-volatility investing compared with higher-risk strategies, and they use risk measures estimated over periods of several years. Others have expressed notes of caution, however. For example, Li, Sullivan, and Garcia-Feijóo [2014] examined long–short strategies using 36-month and 60-month estimation windows, finding that excess returns accrued largely in the first month after portfolio formation. They also found that profits mostly disappeared when low-priced stocks (less than USD 5) were omitted from the investment universe.

For a longer-term perspective, we replicate the U.K. study reported above, replacing short-term risk measures with estimates based on 60 months, as published since 1979 in London Business School’s Risk Measurement Service (Dimson and Marsh [2017]), and following the same estimation procedure for the prior period 1955–1978. We use the same 30:40:30 split of stocks, with percentiles based on number of companies, and weight portfolio returns by market capitalization. We report in Dimson, Marsh, and Staunton [2017] that until the tech bubble burst in 2000, all three risk-based portfolios performed similarly. The fallout after the end of the tech bubble is very obvious, with high-risk stocks performing disastrously. However, since the trough in 2003, high-risk stocks have outperformed. Thus, although low-risk stocks beat high-risk stocks over the full period, their outperformance was entirely attribut-able to the bursting of the tech bubble. This hardly forms the basis of a reliable long-run strategy based on residual risk estimated over longer 60-month rather than shorter 60-day periods.

OTHER FACTORS

In his presidential address to the American Finance Association, Cochrane [2011] referred to a “zoo” of new factors that are related to investment returns. Which factors should be of interest to an investor who is visiting the zoo? As a guide to cutting-edge ideas, many people take the view that there is a market for research, and that the best papers are more likely to appear in the most selective journals with the most demanding review processes. The emergence of profitability, quality, and investment as mainstream factors ref lects the high standing of the journals in which the five-factor model of Fama and French [2015a,b,c,d, 2016] were accepted

for publication. Building on work by Novy-Marx [2013] and others, the authors present a five-factor asset-pricing model that outperforms their earlier three-factor model of stock returns. Surprisingly, when the two additional factors of profitability and investment are added to the original three-factor model, the value factor becomes superf luous in U.S. tests. Consequently, profitability and investment entered the language of academic finance as factors in the return-generating process, and the new five-factor model was referred to by some as a four-factor model.

In a guide for investment practitioners, Berkin and Swedroe [2016] recount five criteria that a poten-tial factor should meet if it is to be useful in practice. A factor must be persistent over time, pervasive across markets, robust to different definitions, intuitive to common sense, and investable at reasonable cost. They note that individual factors may underperform for long intervals, and stress that a long-term strategy is useless if the investor cannot stick to his/her plan. These aspira-tions for factor investing are reasonable, but they also present a challenge. If one waits until compelling evi-dence has accumulated, the investor will be buying after the historical reward to a particular factor has acceler-ated. This has two implications. First, smart beta inves-tors may pay a premium price for factor exposure, which suggests forward-looking returns that are lower than the historical record. Second, other smart beta investors will have made similar portfolio allocations, acquiring factor exposure from asset owners who, on average, have no special factor preference.

Whenever investment strategies are popularized, there is a danger of hitting a reversal point. In the study of U.K. small-cap performance cited earlier, Dimson and Marsh [1999], we compared the large and signifi-cant research-period premium with the post-publication premium. The latter was as large (in absolute terms) and as statistically significant as the former. However, the research-period premium had become negative! On an OOS basis, small caps performed far worse than large caps. Several studies subsequently observed similar pat-terns for other factors. Linnainmaa and Roberts [2016] examine 36 anomalies in stock returns that have been reported in high-quality academic studies and (using new data) examine investment returns before and after the original researchers’ sample periods. The average OOS outperformance from these anomalies was, on average, 58% below the in-sample numbers. McLean and


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Pontiff [2016] examined 97 factors published in top scholarly journals, finding that post-publication returns were (coincidentally) also 58% lower than the published research reported, while correlations with other pub-lished return predictors rose, impairing the diversifica-tion benefits of multifactor investing.

After disappointing performance, amplif ied by transaction costs and management charges, investors seeking to exit from a smart beta strategy run the danger of being involved in crowded trades, with price pressure imposed on exiting investors. Arnott et al. [2016] high-light the danger of overcrowding, which varies with the factor. For example, some commentators have argued that the rapid growth in low-risk investing may have caused these assets to be bid up to an excessive price. Offsetting that is the argument that investors chase good stories and force up the prices of exciting, but volatile, stocks. The “crowd” is not unanimously clamoring for low-volatility investments. If we were to look for an overcrowded segment of the market, it would currently be income stocks. The hunger for income may have elevated the prices of some stocks to an unsustainable level. That would be because many asset owners, not limited to factor investors, want to enhance the income they feel they can draw down from their portfolios.

CONCLUDING REMARKS

We have discussed f ive aspects of factor-based investing that are of great importance to investors. They matter because investment professionals cite strong evidence that they contribute to long-term returns. There is a size effect, in that smaller companies behave differently from large ones. There is a value effect, whereby value stocks perform differently from growth stocks. There is an income effect, with high yielders performing differently from low- and zero yielders, although this may also be regarded as a subset of the value effect. There is a momentum effect, whereby stocks with relative strength generate different returns from stocks with relative weakness. In addition, there is a low-volatility effect that is apparent in some markets. These effects cannot be ignored.

By an “effect,” we mean a tendency for stocks with certain attributes to co-move with one another. For example, value stocks move together, and in a dif-ferent direction to growth stocks. The reason these effects cannot be ignored is that they are likely to have

an impact on portfolio performance. In addition to co-movement, there may also be a premium in expected returns for exposure to these factors. However, the evi-dence on premiums is not conclusive.

Of the factors we have examined, the theoretical case for a return premium is strongest for an expected size premium. Smaller companies are illiquid, their transaction costs are higher, and small-cap portfolios are costly to manage. For other asset classes, lack of marketability is rewarded: illiquid bonds sell at higher yields (i.e., a larger expected return) than comparable liquid instruments, and restricted (large-cap) stocks sell at lower prices than tradable stock in the same company. There is therefore good reason to expect illiquid, smaller companies to also generate larger returns over the long run. Yet although the evidence is consistent with this, the magnitude of the small-firm premium, measured over the longest possible intervals, now appears modest compared with the premiums reported in the original research studies.

Value stocks are typically companies that are distressed or that have impaired business prospects compared with those of growth stocks. High-yielding stocks frequently have the same attributes. They have often become high-yield because their price has fallen. Value stocks are said to be exposed to greater risks of financial distress, and this, according to Fama and French [1993, 1995] is the rationale for their higher expected return. To many researchers, the additional risks accepted by value investors are small compared with the long-run reward from value stocks. Some experts take the view that the gains from value investing are a ref lection of mispricing, whereby enthusiastic investors bid up the prices of growth stocks compared with value stocks. This is a theoretically less appealing foundation for a value premium. Yet the value premium has historically been quite large.

In the past, the income premium and the value premium have been regarded as manifestations of the same underlying attribute: the ratio of security price to a measure of fundamental value. That has changed in the last few years. The hunger for income in a low-return world has pushed up the price of higher-yielding assets. It is not clear whether income stocks have been driven to an unsustainable price or whether there is still headroom for ratings to surge still more for these securities.

Momentum investing is a stock-rotation strategy. Winners do not stay winners forever. The composition


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of a winner portfolio changes rapidly, as does that of a loser portfolio. It is unlikely that we can advance a risk-based explanation for the momentum premium, and the state of theory in this area is rudimentary and exploratory. Johnson [2002], for example, advances a coherent explanation of the momentum premium that is extremely stylized, and Sagi and Seasholes [2007] also offer a rational model of momentum. But although they explain why risk premiums can increase following posi-tive stock returns, they require implausibly high levels of risk aversion to explain the magnitude of momentum profits. Yet despite there being no satisfactory expla-nation for expecting a premium from the momentum factor, there has historically been a large momentum premium over a very long research period. However, the premium is volatile, the strategy is costly to implement, and, as we saw in 2009, a pure WML strategy can lead to major losses from time to time, when momentum traders are whiplashed by a sharp reversal in the market.

Finally, low-risk investing has attracted increased interest from investors. While it is less dynamic than momentum, it nevertheless involves a process of rebal-ancing and recalibration. A key aspect of low-vol investing is that the strategy can achieve returns superior to a blend of the risk-free interest rate and the equity market index. If risk is measured by the CAPM beta, this is the anomaly reported, long ago, by Black et al. [1972]. Why was low-risk investing ignored for so long, with few investment products offered until the onset of the financial crisis? One explanation is that the low-risk factor does not offer a superior return to investors. It offers a superior risk-adjusted return, and this attracted attention only in the past decade. First, low-risk bench-marks were initiated: MSCI launched its minimum-volatility index in 2008, and S&P and Russell followed in 2011, with more than a decade of back data. Second, the stock market suffered a bear market in 2008 and disappointing returns in 2011. During those years, as we saw in Exhibit 1, low-volatility outperformed. Despite absolute returns being relatively poor during the subse-quent market recoveries, investors seem to have acquired a taste for downside protection.

In related work, we study asset returns in dif-ferent monetary conditions over the entire period since 1900 and for all countries for which we have long-term data. As well as looking at a wide variety of assets, both financial (such as stocks and bonds) and real (such as precious metals and artworks) we also examine the

factors proposed by Fama and French [2015a,b,c,d; 2016]. All returns, including five-factor risk premiums, are higher in periods of easing interest rates and lower when rates are tightening. This statistically significant difference in asset returns persists in the U.K. as well as the U.S. We refer the reader to Dimson, Marsh and Staunton [2016] for additional evidence on how factor premiums vary with the interest rate cycle.

Of the various factors, the largest premium has been associated with momentum. This is the factor exposure that most challenges conventional risk-based explanations. The smallest premium is associated with low market capitalization and illiquidity. This is the factor for which all investors might reasonably require some incremental reward. While cogent rationales can be advanced for small-cap and value premiums, the fact is that both effects f luctuate and the small-firm premium has been negative for protracted periods. This should be a warning that, once a premium has been identi-fied and the research disseminated, the rewards to factor exposure may change. Just like predictions of the equity risk premium, it can be dangerous to extrapolate past performance into the future.

Nevertheless, these factors are associated with many styles of portfolio management. The size effect, the value effect, the income effect, the momentum effect, and the low-risk effect remain major inf luences on performance. Portfolio managers may tilt their portfolios toward or away from large-cap stocks, or toward or away from growth stocks. Superior returns may therefore be attributable to one decision regarding size- or value exposure, and may not be a ref lection of the scores of individual transactions executed for the portfolio. For example, a charity client who needs income can inf luence a manager to select high-yielding stocks, which are likely to do well in a period that favors income and value strategies; and a mutual fund facing volatile inf lows and outf lows needs liquid stocks, which are likely to do well in a period that favors large caps.

Almost all investors have investment styles that benefit from or conf lict with momentum. Some indi-vidual investors believe they should let winners run and/or cut losses quickly. Institutional investors are often tempted to window-dress portfolios prior to reporting dates—clearing out losers, for example. In addition, many investors are involuntary momentum traders because they are evaluated relative to a size-based bench-mark, such as the S&P 500 or FTSE 100, and therefore


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buy stocks as they drift above the threshold for index membership. Growth investors buy into stocks that are selling at an increasing price relative to fundamentals. All these market participants are, perhaps unknown to themselves, momentum traders. They transact with others, such as fundamental indexers, who may uncon-sciously be taking a position against momentum. Their investment performance will depend on whether their size and value positions, made in conjunction with their (possibly unintended) exposure to momentum, are suc-cessful. Whatever an investor’s approach, stock-price momentum can inf luence performance.

To conclude: Size, value, income, momentum, and volatility have an important impact on portfolio returns. They should be monitored by all investors.

ENDNOTE

This article was developed in parallel with Chapter 3 of Dimson, Marsh, and Staunton [2017]. Copyright © 2017 Elroy Dimson, Paul Marsh, and Mike Staunton. The authors thank Richard Kersley and The Journal of Portfolio Management editor Frank Fabozzi, as well as participants at a number of industry and academic seminars, for insightful comments. They also thank the Credit Suisse Research Institute for con-tinuing support.

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