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Quality, Factor Momentum, and the Cross-Section of Returns

Quality, Factor Momentum, and the Cross-Section of Returns

Posted June 3, 2024 at 10:31 am
Larry Swedroe
Alpha Architect

The post “Quality, Factor Momentum, and the Cross-Section of Returns” first appeared on Alpha Architect blog.

Of the hundreds of equity factors identified in the financial literature, there were only five that met the criteria Andrew Berkin and I established in our book Your Complete Guide to Factor-Based Investing. To be considered for investment, a factor must have provided a premium that was persistent across long periods and different economic regimes; pervasive across countries, regions, sectors, and even asset classes; robust to various definitions; investable (it held up after implementation costs); and intuitive (there are logical risk-based or behavioral-based explanations for the premium, providing the rationale that it should continue to exist).


Quality (along with the related factor of profitability) was one of the five. Among the traits typically used by practitioners to define a quality company are low earnings volatility, low financial leverage, high gross profitability, high return on equity, low operating leverage, high asset turnover, low accruals (high earnings quality), low equity dilution, and low idiosyncratic stock risk. To capture the multiple dimensions of quality, practitioners commonly define it as a composite. For example, index providers such as MSCI and S&P offer quality indexes that are comprised of at least three underlying quality attributes.

Over the period 1964-2023, the quality premium (quality minus junk, or QMJ) produced an annual premium of 4.7% with a standard deviation of 9.9 and a Sharpe ratio of 0.47. Quality also provided significant diversification benefits, as it had negative correlation to both the market beta (-0.59) and size (-0.50) factors, and relatively low correlation to the value (0.17) and momentum (0.29) factors.

Momentum and Factor Momentum

Momentum is another factor that met the criteria (the others were market beta, size, and value). The evidence has been robust for not only cross-sectional (relative) and time-series (absolute or trend) momentum, but also for factor momentum, which has received much attention from researchers. The empirical research on factor momentum, including the 2019 studies “Factor Momentum Everywhere” and “Is there Momentum in Factor Premia? Evidence from International Equity Markets,” the 2020 study “Factor Momentum and the Momentum Factor,” and the 2021 studies “Factor Momentum,” “Is Factor Momentum More than Stock Momentum?” and “Momentum-Managed Equity Factors,” has examined whether momentum can be found in factors as well and found:

  • Time-series (trend) factor momentum has been a pervasive property of factors—a strategy that buys the recent top-performing factors and sells poor-performing factors achieved significant investment performance above and beyond traditional stock momentum.
  • Factor momentum explained all forms of individual stock momentum—stock momentum strategies indirectly timed factors; they profited when the factors remained autocorrelated and crashed when those autocorrelations broke down.
  • Demonstrating pervasiveness, factor momentum has been a global phenomenon. And cross-country factor momentum exists (if the value factor in Germany recently outpaced the U.S. value factor, it is likely that this trend will continue).
  • Factor momentum could have been captured by trading almost any set of factors.
  • Industry momentum stemmed from factor momentum.
  • The value-added induced by factor management via short-term momentum was a robust empirical phenomenon that survived transaction costs and carried over to multifactor portfolios—while managing factors based on last month’s momentum increased turnover, the increase in turnover induced by timing did not outweigh the benefits of timing. In addition, turnover could have been reduced using a smoothed version of the timing signal, and timing still would have yielded significant benefits.

With these findings in mind, we’ll examine new research that investigated the relationship between the quality factor and fundamental quality momentum (quality acceleration).   

Quality Acceleration

Yao Ma, Baochen Yang, and Tao Ye contribute to the financial literature on factor investing with their study “Quality Acceleration and Cross-Sectional Returns: Empirical Evidence,” published in the April 2024 issue of Research in International Business and Finance, in which they investigated the relationship between quality acceleration and cross-sectional returns. They measured the quality of a firm through four dimensions: profitability, growth, payout, and safety. The profitability dimension consisted of four proxies: return on assets (ROA), return on equity (ROE), gross profits-to-assets (GPOA), and gross profit margin (GMAR). Their growth dimension consisted of four proxies: growth in return on assets (DROA), growth in return on equity (DROE), growth in gross profits-to-assets (DGPOA), and growth in gross profit margin (DGMAR). Their payout dimension consisted of three proxies: net equity issuance (NEI), net debt issuance (NDI), and net payout ratio (NP). Their safety dimension consisted of three proxies: market beta (Beta), leverage (LEV), and idiosyncratic volatility (IVOL). They then combined the four dimensions into a quarterly quality score to measure the quality level: Quality = Z(Profitability + Growth + Payout + Safety). They defined quality acceleration as the quarter-on-quarter change in quality growth. They then categorized stocks into 30th and 70th deciles. Their data sample covered Chinese stocks and the period 2004-2019. Following is a summary of their key findings:

  • The hedge portfolio of buying the stocks with the highest quality acceleration and selling the stocks with the lowest quality acceleration delivered significant excess returns (alphas) of 0.49% per month equal-weighted (t-stat = 2.81) and 0.69% value-weighted (t-stat = 3.31). These returns were still significant after being adjusted by the Fama and French three-factor model (alpha of 0.52%, t-stat of 3.07), the Carhart four-factor model (alpha of 0.51%, t-stat of 2.88), and the Fama-French five-factor model (alpha of 0.44%, t-stat of 2.20)—the predictive power could not be explained by common risk factors.
  • The quality acceleration premium came mainly from the short leg of the quality acceleration hedge portfolio.
  • The quality acceleration effect was robust after considering several firm characteristics associated with cross-sectional returns—the predictive ability of quality acceleration on subsequent returns was not subsumed by other firms’ characteristics.
  • The prediction information contained in quality acceleration was not subsumed by either quality level or quality growth.
  • The quality acceleration effect gradually weakened after three months but did not reverse in the long term.
  • The quality acceleration effect was stronger during the period of high investor sentiment, becoming weaker or even disappearing during the period of low investor sentiment—the difference between the quality acceleration premium in the periods of both high investor sentiment and low investor sentiment was economically and statistically significant.
  • Using four proxies of limits to arbitrage (firm size, illiquidity, idiosyncratic volatility, and institutional ownership), there was no significant correlation between the quality acceleration effect and limits to arbitrage.
  • There was no significant relationship between the quality acceleration effect and investor attention.

These findings are consistent with those of Shuoyuan He and Ganapathi Narayanamoorthy, authors of the 2019 study “Earnings Acceleration and Stock Returns,” who found that earnings acceleration, defined as the quarter-over-quarter change in earnings growth, has significant explanatory power for future excess returns in U.S. stocks.

Their findings led Ma, Yang, and Ye to conclude: “Quality acceleration has been a strong predictor of subsequent stock cross-sectional returns in the Chinese stock market.” They added: “Investors could underreact to the information contained in quality acceleration, leading to behavioral mispricing, which enables quality acceleration to predict future stock returns in the short term. However, as the information about quality acceleration is gradually discovered and this mispricing is gradually corrected, so the quality acceleration effect disappears in the long term.” Explaining their findings: “Our study provides strong evidence that investor sentiment can explain the quality acceleration effect, and the higher the investor sentiment is, the stronger the quality acceleration effect is. This is due to the more serious irrational behavior of individual investors during the period of high investor sentiment, leading to greater mispricing.”

Investor Takeaways

The empirical evidence we have reviewed demonstrates that quality acceleration plays a role in predicting future stock returns, and the prediction information contained in quality acceleration has not been subsumed by quality level and quality growth, and even exceeds the prediction information contained in them. In addition, quality acceleration captures information beyond the common anomalies.

There is also strong empirical evidence demonstrating that momentum (both cross-sectional and time-series) provides information on the cross-section of returns of many risk assets and has generated alpha relative to existing asset pricing models. Ma, Yang, and Ye’s findings provide another test of both robustness and pervasiveness, increasing our confidence that the findings of momentum in asset prices are not a result of data mining.

The strong empirical evidence is why firms like Alpha ArchitectAQRAvantisBridgeway, and Dimensional, leaders in factor-investing strategies, incorporate momentum into their strategies. For example, based on research demonstrating the persistence of factor momentum, AQR recently added cross-sectional stock market factor momentum to their general managed futures strategy. Individual investors can utilize momentum strategies without incurring additional costs by incorporating momentum into trading decisions. For example, when rebalancing, they can delay purchases of assets with negative momentum and delay sales of assets with positive momentum.

Larry Swedroe is the author or co-author of 18 books on investing, including his latest, Enrich Your Future: The Keys to Successful Investing.


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