ROA Effect within Stocks

Fundamental reason

Research explains that firms with productive assets should yield higher average returns than firms with unproductive assets. Productive firms for which investors demand high average returns should be priced similarly to less productive firms for which investors demand lower returns. Variation in productivity, therefore, helps identify variation in investors’ required rates of return. Therefore profitable firms generate higher average returns than unprofitable firms (as productivity helps identify this variation - with higher profitability indicating higher required rates). This fact motivates the return-on-asset factor.

Simple trading strategy

The investment universe contains all stocks on NYSE and AMEX and Nasdaq with Sales greater than 10 million USD. Stocks are then sorted into two halves based on market capitalization. Each half is then divided into deciles based on Return on assets (ROA) calculated as quarterly earnings (Compustat quarterly item IBQ - income before extraordinary items) divided by one-quarter-lagged assets (item ATQ - total assets). The investor then goes long the top three deciles from each market capitalization group and goes short bottom three deciles. The strategy is rebalanced monthly, and stocks are equally weighted.

Out-of-sample strategy's implementation/validation in QuantConnect's framework(chart, statistics & code)

Source paper

Chen, Zhang: A Better Three-Factor Model That Explains More Anomalies

Abstract: The market factor, an investment factor, and a return-on-assets factor summarize the cross-sectional variation of expected stock returns. The new three-factor model substantially outperforms traditional asset pricing models in explaining anomalies associated with short-term prior returns, financial distress, net stock issues, asset growth, earnings suprises, and valuation ratios. The model's performance, cobined with its economic intuition based on q-theory, suggests that it can be used to obtain expected return estimation in practice.

Other papers

• Chen, Novy-Marx, Zhang: An Alternative Three-Factor Model

  Abstract: We propose a new factor model consisting of the market factor, an investment factor, and a return on assets factor for explaining the cross-section of expected stock returns. The new factor model outperforms traditional asset pricing models in explaining anomalies such as those associated with short-term prior returns, failure probability, O-score, earnings surprises, accruals, net stock issues, and stock valuation ratios. The new model's performance, combined with its economic intuition, suggests that it can be used to obtain expected return estimates in practice.

• Bouchaud, Stefano, Landier, Simon, Thesmar: The Excess Returns of 'Quality' Stocks: A Behavioral Anomaly

  Abstract: This note investigates the causes of the quality anomaly, which is one of the strongest and most scalable anomalies in equity markets. We explore two potential explanations. The "risk view", whereby investing in high quality firms is somehow riskier, so that the higher returns of a quality portfolio are a compensation for risk exposure. This view is consistent with the Efficient Market Hypothesis. The other view is the "behavioral view", which states that some investors persistently underestimate the true value of high quality firms. We find no evidence in favor of the "risk view": The returns from investing in quality firms are abnormally high on a risk-adjusted basis, and are not prone to crashes. We provide novel evidence in favor of the "behavioral view": In their forecasts of future prices, and while being overall overoptimistic, analysts systematically underestimate the future return of high quality firms, compared to low quality firms.

• Lu, Stambaugh, Yuan: Anomalies Abroad: Beyond Data Mining

  Abstract: A pre-specified set of nine prominent U.S. equity return anomalies produce significant alphas in Canada, France, Germany, Japan, and the U.K. All of the anomalies are consistently significant across these five countries, whose developed stock markets afford the most extensive data. The anomalies remain significant even in a test that assumes their true alphas equal zero in the U.S. Consistent with the view that anomalies reflect mispricing, idiosyncratic volatility exhibits a strong negative relation to return among stocks that the anomalies collectively identify as overpriced, similar to results in the U.S.

• Liang, Tang, Xu: Uncertainty, Momentum, and Profitability

  Abstract: In this article, the authors argue that momentum and profitability factors share a common source in uncertainty. Specifically, the authors find that uncertainty subsumes price momentum and operating profitability; it also accounts for the majority of the profits associated with earnings momentum and return on equity, especially in large firms. Further, the profits of these four aforementioned momentum/profitability strategies concentrate in periods of negative market returns, consistent with high uncertainty stocks’ greater vulnerability to bad market states documented in recent literature. The market-state dependence of momentum/profitability strategies has significant implications to portfolio managers who attempt to profit from these strategies. Understanding the sources of the profits also helps portfolio managers better employ these factors in constructing investment portfolios.

• Raju: Implementing a Systematic Long-only Quality Strategy in the Indian Market

  Abstract: We believe investors should be willing to pay a higher price for higher quality companies. We build a composite quality score using 'off -the-shelf' criteria and publicly available financial data and show that a quarterly-rebalanced, long-only portfolio of 12 stocks selected using our score in India significantly outperforms the NIFTY 100 Index - both in terms of absolute returns (by 5.50% pa) and risk adjusted returns - while having an acceptable annual turnover (a modal turnover of 41.67%). We show that our quality score predicts the persistence of quality for up to 3 years and there is a weak relationship between the price multiple and the quality score. We show that ESG criteria can be incorporated into a quality measure. Furthermore, we demonstrate that quality needs to be reviewed regularly - so a buy-and-hold approach may not be an ideal strategy for an investor. In the absence of cheap ETFs to get systematic exposure to quality, the systematic long-only strategy using 'off -the-shelf' criteria provides a practical, executable systematic investment methodology that exposes an investor to quality in the Indian market. Hsu, Kalesnik, Kose: What Is Quality?<br data-rich-text-line-break="true" />https://www.tandfonline.com/doi/full/10.1080/0015198X.2019.1567194<br data-rich-text-line-break="true" />Abstract:<br data-rich-text-line-break="true" />Unlike standard factors, such as value, momentum, and size, “quality” lacks a commonly accepted definition. Practitioners, however, are increasingly gravitating to this style factor. They define quality to be various signals or combinations of signals—some that have been thoroughly explored in the academic literature and others that have received limited attention. Among a comprehensive group of the quality categories used by practitioners, we find that profitability, accounting quality, payout/dilution, and investment tend to be associated with a return premium whereas capital structure, earnings stability, and growth in profitability show little evidence of a premium. Profitability and investment-related characteristics tend to capture most of the quality return premium.

• Carl Johan Ingvarsson: Quality’s relationship to the idiosyncratic volatility puzzle

  Abstract: This paper examines the well documented relationship between idiosyncratic volatility and mean returns. By using the recently published quality-minus-junk factor this paper attempts to explain both the abnormal performance of portfolios sorted on idiosyncratic volatility as well as the crosssectional pricing of idiosyncratic volatility. Using data from the U.S. it is shown that the quality factor is able to explain the abnormal performance of the extreme portfolios in the idiosyncratic volatility puzzle, while having no impact on the cross-sectional stock returns. This indicates that the quality-minus-junk factor plays an important role in determining the performance of the portfolios and further research should include it in any model aiming to investigate this puzzle.