Given recent performance, the question of whether small-cap stocks really do outperform over time has made its way into the financial media. So far, we’ve sought to answer it by considering a multifactor approach and examining international evidence. Today we’ll tackle a behavioral explanation.
The field of behavioral finance provides us with an explanation for the small growth stock anomaly. It exists because investors have a preference for “lottery tickets.”
Nicholas Barberis and Ming Huang—the authors of the NBER working paper, “Stocks as Lotteries: The Implications of Probability Weighting for Security Prices”—found that:
- Investors have a preference for securities that exhibit positive skewness, which occurs in cases where values to the right of (more than) the mean are fewer but farther from it than values to the left of (less than) the mean. Such investments provide the small chance of a huge payoff (winning the lottery). Investors find this small possibility attractive. The result is that positively skewed securities tend to be “overpriced”—they earn negative average excess returns.
- The preference for positively skewed assets explains the existence of several anomalies (deviations from the norm) to the efficient market hypothesis, including the low average return on IPOs, private equity and distressed stocks, despite their high risks.
In theory, we would expect anomalies to be arbitraged away by investors who don’t have a preference for positive skewness. They should be willing to accept the risks of a large loss for the higher expected return that shorting overvalued assets can provide. However, in the real world, anomalies can persist because there are limits to arbitrage.
First, many institutional investors, such as pension plans, endowments and mutual funds, are prohibited by their charters from taking short positions.
Second, the cost of borrowing a stock in order to short it can be expensive, and there can be a limited supply of such stocks available to short.
Third, most investors are unwilling to accept the risks associated with shorting because of the potential for unlimited losses. This is prospect theory at work. The pain of a loss is felt more deeply than the joy of an equal gain.
Fourth, short-sellers run the risk that borrowed securities are recalled before the strategy pays off, as well as the risk that the strategy performs poorly over the short run, triggering an early liquidation.
Taken together, these factors suggest that investors may be unwilling to trade against the overpricing of skewed securities. This allows the anomaly to persist.
The conclusion we can draw is that the issue of the size premium’s “disappearance” may be a function of this “black hole” rather than something that impacts the entire asset class. If you screened out the “black hole” stocks, there would be a size premium that could be captured. Said another way, it’s the higher-quality small stocks that explain the size premium.
Controlling For Quality
Cliff Asness, Andrea Frazzini, Ronen Israel, Tobias Moskowitz and Lasse Pedersen—authors of the January 2015 paper, “Size Matters, If You Control Your Junk”—examined the problem of the disappearing size premium by controlling for the quality factor.
They note: “Stocks with very poor quality (i.e., ‘junk’) are typically very small, have low average returns, and are typically distressed and illiquid securities. These characteristics drive the strong negative relation between size and quality and the returns of these junk stocks chiefly explain the sporadic performance of the size premium and the challenges that have been hurled at it.”
High-quality stocks have the following characteristics: low earnings volatility, high margins, high asset turnover, low financial and operating leverage, and low idiosyncratic risk. The research shows these types of stocks—the kind that Benjamin Graham and Warren Buffett have long advocated buying—outperform low-quality stocks with opposite characteristics (those “lottery-ticket” equities).
The authors additionally found that “small quality stocks outperform large quality stocks and small junk stocks outperform large junk stocks, but the standard size effect suffers from a size-quality composition effect.” In other words, controlling for quality restores the size premium.
The authors thus concluded that the challenges to the size premium “are dismantled when controlling for the quality, or the inverse ‘junk,’ of a firm. A significant size premium emerges, which is stable through time, robust to the specification, more consistent across seasons and markets, not concentrated in microcaps, robust to non-price based measures of size, and not captured by an illiquidity premium. Controlling for quality/junk (the QMJ factor) also explains interactions between size and other return characteristics such as value and momentum.”
They further found that “controlling for junk produces a robust size premium that is present in all time periods, with no reliably detectable differences across time from July 1957 to December 2012, in all months of the year, across all industries, across nearly two dozen international equity markets, and across five different measures of size not based on market prices.”
They also note: “When adding QMJ as a factor, not only is a very large difference in average returns between the smallest and largest size deciles observed, but, perhaps more interestingly, there is an almost perfect monotonic relationship between the size deciles and the alphas. As we move from small to big stocks, the alphas steadily decline and eventually become negative for the largest stocks.”
Another important finding was that higher-quality stocks were more liquid, which has important implications for portfolio construction and implementation.
Asness, Frazzini, Israel, Moskowitz and Pedersen found similar results when, instead of controlling for the quality factor, they controlled for the low-beta factor. High-beta stocks (again, those lottery tickets) have very poor historical returns. And those high-beta stocks tend to be the same low-quality stocks.
In addition, they found that small stocks have negative exposure to two relatively new factors, the profitability factor (referred to as RMW, or robust minus weak); and the investment factor (referred to as CMA, or conservative minus aggressive). High-profitability firms outperform low-profitability ones and low-investment firms outperform high-investment ones.
As a final note, of interest is that the new Q-factor model (which includes the four factors of beta, size, profitability and investment) proposed in the 2012 paper “Digesting Anomalies: An Investment Approach” is able to explain almost all the anomalies that plague the prior workhorse model, the Fama-French four-factor model (beta, size, value and momentum). The sole, big exception is the poor performance of small growth stocks with low profitability.
In summary, Asness, Frazzini, Israel, Moskowitz and Pedersen concluded that “size matters—and in a much bigger way than previously thought—but only when controlling for junk. Controlling for junk, a much stronger and more stable size premium emerges that is robust across time, including those periods where the size effect seems to fail; monotonic in size and not concentrated in the extremes; robust across months of the year; robust across non-market price based measures of size; not subsumed by illiquidity premia; and robust internationally. These results are robust across a variety of quality measures as well.”
Currently, there are many passively managed small-cap mutual funds that include screens that minimize or eliminate exposure to the small junk stocks and overweight the quality ones. These funds allow investors to benefit from the latest academic research.
The bottom line is that today’s investors can purchase funds that include only the “good” small value stocks, or they can buy funds that screen out the “bad” small value stocks (avoiding small stocks with characteristics such as high investment, low profitability and poor momentum), or they can order up a happy combo-platter of both.
Larry Swedroe is the director of research for The BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.