Profitable firms have more significant market returns, then unprofitable firms. This is something that matches common sense logic of everyday life. But the Effective Market Theory was telling us for a long time that there shouldn‘t be any return difference between profitable and unprofitable firms as factor of profit should be already fully priced into the price of stock.
However, recent academic studies confirm what every Wall-Streeter (and also Main-Streeter) already knew. They show there is a robust and strong return premium in holding profitable stocks and so it makes sense to go long firms with strong ROA (Return on Assets) and short firms with weak ROA. Source paper for this effect also shows that the ROA effect could explain a lot of other anomalies (mainly earnings and profitability related – like popular price-to-earnings ratio, etc.). The strategy is built as a long-short portfolio and for example, is using thousands of stocks in an investment portfolio, but it is indeed possible to exploit this effect also in a smaller portfolio.
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.
Confidence in anomaly's validity
Backtest period from source paper
Notes to Confidence in Anomaly's Validity
Period of Rebalancing
Notes to Indicative Performance
per annum, annualized (geometrically) monthly return of 0.96%, data from table 1
Notes to Period of Rebalancing
Number of Traded Instruments
Notes to Estimated Volatility
estimated from t-statistic 5.10, data from table 1
Notes to Number of Traded Instruments
more or less, it depends on investor’s need for diversification
Notes to Maximum drawdown
Notes to Complexity Evaluation
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.
Hedge for stocks during bear markets
Not known - Source and related research papers don’t offer insight into correlation structure of proposed trading strategy to equity market risk, therefore we do not know if this strategy can be used as a hedge/diversification during time of market crisis. Strategy is built as a long-short, but it can be split into 2 parts. Long leg of strategy is surely strongly correlated to equity market however short-only leg can be maybe used as a hedge during bad times. Rigorous backtest is however needed to determine return/risk characteristics and correlation.
Chen, Zhang: A Better Three-Factor Model That Explains More Anomalies
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.
Strategy's implementation in QuantConnect's framework
Chen, Novy-Marx, Zhang: An Alternative Three-Factor Model
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
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
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
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.