Quality Factor Investing: What the Research Shows

Quality factor investing tilts a portfolio toward companies with strong, durable fundamentals, most often high profitability, on the evidence that such firms have earned higher average returns than weak ones. The best-known studies are Novy-Marx on gross profitability, Asness, Frazzini and Pedersen on quality minus junk, and the profitability factors in the Fama-French and q-factor models. Their headline numbers are real but mostly in-sample, and we found no quality-specific evidence on how they have held up since publication.

Quality Factor Investing by the Numbers

What “quality” means in factor research

There is no single definition of quality. Researchers have measured quality as gross profits over assets, as a composite of profitability, growth and safety, as operating profitability, as return on equity, and as a score built from accounting signals. They all try to isolate the same idea: firms that earn more on what they own have, on average, been rewarded with higher returns than their prices implied.

That idea sits oddly next to value investing, which buys cheap stocks, because profitable firms are often not cheap. As the sections below show, several of the best-known papers argue that quality and value complement each other rather than compete. For the wider family of factors, see our overview of factor investing.

Gross profitability: the other side of value

Robert Novy-Marx’s paper on the gross profitability premium was published in the Journal of Financial Economics in 2013 (108(1), 1–28). The figures in this section come from the NBER working-paper version (w15940), whose sample runs from July 1963 to December 2009 and excludes financial firms. The published version was not checked and may use slightly different numbers.

The working paper’s abstract says that profitability, measured as gross profits to assets, “has roughly the same power as book-to-market predicting the cross-section of average returns.” Profitable firms earned significantly higher average returns than unprofitable ones even though they tended to have lower book-to-market ratios and larger market capitalizations. In the sorts, the most profitable firms earned 0.33% a month more than the least profitable (t = 2.63), and the spread’s alpha against the Fama-French three-factor model was 0.55% a month (t = 4.75).

Why gross profit rather than earnings? Novy-Marx argues that gross profit is “the cleanest accounting measure of true economic profitability” and that measures further down the income statement become more polluted. He frames profitable-firm strategies as growth strategies that hedge value, which is why the paper calls profitability “the other side of value.” The abstract also reports that controlling for profitability improves value strategies, especially among the largest and most liquid stocks.

Quality minus junk: profitability, growth and safety

Asness, Frazzini and Pedersen (2019) broadened the idea. Their paper, “Quality minus junk,” defines quality as the characteristics investors should be willing to pay a higher price for, and the published abstract names three: profitability, growth and safety. An earlier working-paper draft from October 2013 averaged four composite proxies, adding payout. Whether payout survives in the published score was not verified, so treat the exact recipe as version-dependent.

The published abstract reports that a quality-minus-junk (QMJ) factor, long high-quality and short low-quality stocks, “earns significant risk-adjusted returns in the United States and across 24 countries.” It also finds that quality is only modestly priced, that the price of quality varies over time and reached a low during the internet bubble, and that a low price of quality predicts a high future QMJ return.

The numbers in our table for this strategy come from the working-paper draft, not the published article. In that draft the high-minus-low quality decile spread earned 47 to 68 basis points a month depending on the sample (t = 2.80 to 3.22).

Profitability in factor models: RMW and the ROE factor

Profitability entered the mainstream factor models in two ways. Fama and French (2015), in “A five-factor asset pricing model,” added profitability and investment factors to market, size and value. In Kenneth French’s data library, RMW (robust minus weak) is the average return on two robust operating-profitability portfolios minus the average return on two weak ones, built from 2×3 size and profitability sorts. The monthly series starts in July 1963.

The authors’ November 2013 draft says the model is rejected by the GRS test but “for applied purposes it provides an acceptable description of average returns.” The same draft found that, in the five-factor model, “HML is redundant for explaining average returns.” Treat that as a draft, sample-specific finding, not settled doctrine. We did not check the published text of either quote.

Hou, Xue and Zhang (2015) reached a related place from the investment side. Their q-factor model has market, size, investment and profitability (ROE) factors, and they report that it “largely summarizes the cross section of average stock returns.” Their ROE is income before extraordinary items divided by one-quarter-lagged book equity. Over January 1972 to December 2012 the high-minus-low ROE factor averaged 0.58% a month (t = 4.81).

Scores and screens: Piotroski, accruals and Greenblatt

Joseph Piotroski (2000) took a more practical route. His F_SCORE is the sum of nine binary signals drawn from historical financial statements, so it runs from 0 to 9. In his abstract, a strategy that buys expected winners and shorts expected losers among high book-to-market firms generated a 23% annual return between 1976 and 1996. That figure is gross, in-sample and long-short, and it applies only to high book-to-market stocks. Piotroski also reports that the benefits are concentrated in small and medium-sized firms, firms with low share turnover and firms with no analyst following.

Accruals are a related quality signal. Sloan (1996) found that stock prices act as if investors “fixate” on earnings and fail to reflect fully what accruals and cash flows say about future earnings. His hedge portfolio, long the lowest-accrual firms and short the highest, returned 10.4% (t = 4.71) in the year after formation, size-adjusted. We cover the mechanics in earnings quality and accruals.

Finally, Joel Greenblatt’s 2005 book The Little Book That Beats the Market popularized the “magic formula,” which, as Wikipedia describes it, ranks stocks on earnings yield (EBIT over enterprise value) and return on capital (EBIT over net fixed assets plus working capital). We cite no returns for it. The same secondary source summarizes third-party tests as mixed, noting increased volatility and short-term underperformance alongside outperformance in several markets, and we did not verify those underlying studies.

How Alpha Suite uses this

Alpha Suite’s Quality Tilt strategy ranks about 100 non-financial stocks on quality (return on assets, capped return on equity and operating margin) and uses value only as a floor. Our own point-in-time backtest of that rule found no statistically significant excess return, and we explain how the test was built and what it taught us in our point-in-time backtest write-up.

Limitations

The headline numbers above are mostly in-sample, mostly long-short, and several come from working papers rather than the published versions. Long-short spreads also hide the cost and difficulty of shorting, and Piotroski’s 23% is explicitly gross. We did not verify any quality-specific evidence on how these premiums behaved after publication, for instance QMJ after 2012 or RMW after the Fama-French paper, so we cannot tell you whether they have faded. The absence of that evidence in our sources is not evidence that they have held up.

The general replication literature is a reason for caution. Hou, Xue and Zhang (2020) found that 65% of 452 anomalies cannot clear a single-test hurdle of an absolute t-value of 1.96, and that even for replicated anomalies the economic magnitudes are “much smaller than originally reported.” That is general anomaly evidence, not a finding about quality specifically, but it is the right prior for any published premium. Definitions also differ across papers (gross profits, operating profitability, ROE, composite scores), so “quality works” is really several overlapping claims. Overfitting a quality rule to a sample is a live risk, discussed in overfitting in backtesting. For how quality tilts compare with other factors, see the low-volatility anomaly and momentum investing. This article is research, not investment advice.

FAQ

What is quality factor investing?

It is a factor approach that favors companies with strong fundamentals, such as high gross profitability, and avoids weak ones. Definitions vary by paper, from gross profits over assets to composites of profitability, growth and safety.

What is the gross profitability premium?

Novy-Marx found that firms with higher gross profits relative to assets earned higher average returns than less profitable firms. In the NBER working paper (July 1963 to December 2009) the spread was 0.33% a month, and gross profitability predicted returns roughly as well as book-to-market.

How is QMJ different from the Fama-French RMW factor?

RMW is a single-characteristic factor, robust minus weak operating profitability. The published QMJ factor combines profitability, growth and safety (the working paper also included payout) into one quality score.

Does the Piotroski F-score still work?

We did not verify post-publication evidence either way. The 23% annual return in the original paper is gross, in-sample and long-short, covers 1976 to 1996, and applies only to high book-to-market firms.

References

1. Asness, C. S., Frazzini, A. & Pedersen, L. H. (2019). “Quality minus junk.” Review of Accounting Studies, 24(1), 34–112. doi:10.1007/s11142-018-9470-2
2. Fama, E. F. & French, K. R. (2015). “A five-factor asset pricing model.” Journal of Financial Economics, 116(1), 1–22. doi:10.1016/j.jfineco.2014.10.010
3. Greenblatt, J. (2005). The Little Book That Beats the Market. John Wiley & Sons. Described via Wikipedia, “Magic formula investing.”
4. Hou, K., Xue, C. & Zhang, L. (2015). “Digesting Anomalies: An Investment Approach.” Review of Financial Studies, 28(3), 650–705. doi:10.1093/rfs/hhu068
5. Hou, K., Xue, C. & Zhang, L. (2020). “Replicating Anomalies.” Review of Financial Studies, 33(5), 2019–2133. doi:10.1093/rfs/hhy131
6. Novy-Marx, R. (2013). “The other side of value: The gross profitability premium.” Journal of Financial Economics, 108(1), 1–28. doi:10.1016/j.jfineco.2013.01.003 (figures here are from the NBER working paper w15940, doi:10.3386/w15940)
7. Piotroski, J. D. (2000). “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers.” Journal of Accounting Research, 38(Supplement), 1–41. doi:10.2307/2672906
8. Sloan, R. G. (1996). “Do Stock Prices Fully Reflect Information in Accruals and Cash Flows About Future Earnings?” The Accounting Review, 71(3), 289–315. doi:10.2308/tar-9608042309
9. Kenneth R. French Data Library, “Description of Fama/French 5 Factors (2x3).” mba.tuck.dartmouth.edu