So far, this chapter has presented B/M, E/P, CF/P and D/P anomalies in addition to momentum and short-term reversal anomalies. Technically, if B/M, E/P, CF/P and D/P ratios are proxies for risk—rather than for mispricing—as suggested by Fama and French (1993, 2004), then these ratios are not anomalies, but instead risk factors. For consistency, the remaining of this thesis refers to all long-minus-short strategies as factors regardless of whether they capture mispricing or risk. The remaining of this chapter reviews asset growth, operating profitability, betting against beta, quality minus junk and a refined value factor.
Cooper, Gulen and Schill (2008) find that asset growth rates, measured as the annual changes in total assets, have a strong predictive power on future stock returns. The correlation between a firm’s asset growth and its subsequent market return is negative and statistically significant. Firms with the lowest asset growth rates earn abnormally high returns, and firms with the highest asset growth rates earn abnormally low returns.
Cooper et al. rank stocks annually to ten decile portfolios based on their asset growth rates from the end of year t-2 to the end of year t-1. The monthly average return to buying the bottom decile and selling the top decile, rebalancing the portfolio annually, is 1.73% using equal-weighted portfolios and 1.05% using value-weighted portfolios.
Novy-Marx (2013) finds that profitable firms have significantly higher average returns than unprofitable firms. Novy-Marx measures the profitability using a ratio of gross profits-to-assets, where gross profits are defined as the total revenue minus cost of goods sold. The gross profitability has a similar explanatory power on the cross-section of average returns as the B/M ratio. The portfolios that are formed on gross profits-to-assets have both value and growth characteristics because profitable firms have low B/M ratios, and unprofitable firms have high B/M ratios. Novy-Marx suggests that the measure of gross profitability is best combined with value strategies because value and gross profitability are negatively correlated. Sorting stocks on both value and gross profitability results in a strategy that buys profitable value firms and sells unprofitable growth firms. The volatility of the combined value and gross profitability strategy is lower than for standalone strategies.
Fama and French (2015) adapt profitability in their five-factor model using operating profitability (OP), which is defined as the revenue minus cost of goods sold, minus selling, general and administrative expenses, minus interest expense and divided by book equity.
The investment factor in the five-factor model is identical to the asset growth of Cooper et al. (2008) as both factors measure the change in total assets from the end of year t-2 to the end of year t-1.
Frazzini and Pedersen (2014) introduce a betting against beta (BAB) strategy that buys low beta stocks and sells high beta stocks. Because both individual and institutional investors are commonly subject to leverage and margin constraints, Frazzini and Pedersen suggest that investors overweight high-beta stocks, which in turn lowers their expected returns in comparison to low-beta stocks. The BAB strategy is constructed by buying low-beta stocks and then leveraging the total beta of the long position to one and selling high-beta stocks and then de-levering the total beta of the short position to one.
The resulting strategy is a zero-cost strategy and has a beta of zero.
Asness and Frazzini (2013) suggest that a monthly rebalanced HML factor (HMLD), which uses the current market value of equity, is a better proxy for value than the one proposed by Fama and French (1992). Fama and French construct and rebalance the HML factor annually at the end of June and use six months lagged information of book equity and market value to ensure that the accounting information for a fiscal year preceding the portfolio construction would have been publicly available at the time. The HML factor is therefore based on information that is always at least six months old, and just before the next rebalancing, the information is 18 months old. Asness and Frazzini (2013) find that rebalancing the HML factor monthly and using the contemporaneous market value of equity yields a better proxy for the actual ex-post B/M ratio. The monthly updated measure of value also outperforms the annually updated factor when used together with momentum.
Asness, Frazzini and Pedersen (2019) find that high-quality stocks, defined in terms of high profitability, high prior growth and safety, generate on average higher risk-adjusted returns than low-quality stocks with the opposite characteristics. The authors measure quality using a composite score of profitability, growth and safety. Asness et al. follow the methodology of Asness and Frazzini (2013), and sort stocks first on size and then on quality. The quality minus junk (QMJ) factor return is obtained by subtracting the average return of two low-quality portfolios from the average return of two high-quality portfolios.
Asness et al. (2019) do not find any evidence of quality stocks bearing higher risk than junk stocks. The quality stocks have low market betas, and they tend to perform well in market downturns when investors prefer quality over uncertainty. The authors find that analysts’ target prices are higher for high-quality stocks than they are for low-quality stocks, but the analysts tend to underestimate the return potential of high-quality stocks.
Asness et al. conclude that quality stocks outperform junk stocks either because quality stocks are underpriced and junk stocks overpriced, or because quality stocks are exposed to an unknown risk factor.