Swedroe: Differing Definitions Of Quality

Swedroe: Differing Definitions Of Quality

Academics and the investment industry differ on what defines ‘quality,’ but which definition performs better?

Reviewed by: Larry Swedroe
Edited by: Larry Swedroe

The 1997 publication of Mark Carhart’s paper, “On Persistence in Mutual Fund Performance,” led to the four-factor model (which added momentum to market beta, size and value) becoming the workhorse model in finance. The next major contribution came from Robert Novy-Marx. His 2012 paper, “The Other Side of Value: The Gross Profitability Premium,” provided investors not only with new insights into the cross section of stock returns, but it helped further explain Warren Buffett’s superior performance.

Researchers have since extended the profitability factor to a broader quality factor (the returns to high-quality companies minus the returns to low-quality companies) that captures a larger set of quality characteristics. While there is not yet one consistent definition of quality, in general, high-quality companies have the following traits: low earnings volatility, high margins, high asset turnover (indicating the efficient use of assets), low financial leverage, low operating leverage (indicating a strong balance sheet and low macroeconomic risk) and low specific stock risk (volatility unexplained by macroeconomic activity).

Companies with these characteristics historically have provided higher returns, especially in down markets. In particular, high-quality stocks that are profitable, stable, growing and have a high payout ratio outperform low-quality stocks with the opposite traits.

Mutual fund companies have been active in developing investment vehicles that provide access to this new factor. And cash flows have followed. Quality is among the hot “smart beta” strategies into which investors are pouring assets.

Academics Vs. ‘The Industry’
Georgi Kyosev, Matthias Hanauer, Joop Huij and Simon Lansdorp contribute to the literature on the quality factor with their June 2016 paper, “Quality Investing — Industry versus Academic Definitions.” Their data sample covers developed and emerging market stocks starting from December 1985 and December 1992, respectively, until December 2014, and an average of about 1,600 stocks from the FTSE World Developed Index and from the S&P/IFC Investable Emerging Markets Index.

The authors chose to limit their universe because “many return anomalies are known to disappear or become significantly less pronounced when the universe is restricted to large-caps.” Thus, they restricted their emerging market universe to the 500 largest stocks.

In their analysis, the authors created two categories of quality companies. The first category they referred to as “industry” quality. Based on their review of fund prospectuses, index methodologies and research notes, the authors selected a representative but nonexhaustive list of “industry” variables consisting of return on equity (ROE), earnings to sales (margin), 12 months’ growth in return on equity (ROE growth), total debt to common equity (leverage) and volatility of earnings growth (earnings variability). The second category they referred to as “academic” quality. Based on robust documentation in the academic literature, they selected:


  • Gross profitability: Revenue minus cost of goods sold
  • Accruals: Firms with high accruals earn abnormally lower average returns than firms with low accruals, because investors overestimate the persistence of the accrual component of earnings when forming earnings expectations.
  • Net stock issues: Net stock issuance and stock returns are negatively correlated as smart managers issue shares when sentiment-driven traders push prices to overvalued levels.

Separating stocks into quintiles, the authors constructed equal-weighted, long/short (long the top quintile and short the bottom quintile) portfolios for the two quality measures (industry and academic) and rebalanced monthly. Following is a summary of their findings on the industry quality measures:

  • All five industry quality measures produced positive returns for the top-bottom portfolios, with the most commonly used metric (ROE) showing the largest premium at 3.1% (with a t-statistic of 1.5). The equal-weighted industry measure produced a premium of 2.3% (with a t-statistic of 1.1). However, none of the premiums were statistically significant at the 5% confidence level.
  • Controlling for the four factors (market beta, size, value and momentum) in the Carhart model, the strongest variable, ROE, had positive loadings on the value and momentum factors and an alpha of 2.6%. But with a t-statistic of 1.5, it was not statistically significant at the 5% confidence level. The combined “industry” quality strategy also loaded on value and momentum and produced an alpha of 3.1%, with a t-statistic of 1.9 (close to being statistically significant at the 5% confidence level). The one quality variable that stood out was earnings variability, with a statistically significant alpha of 3.5% (and t-statistic of 3.0). Its market beta loading of -0.3 (with a t-statistic of -11.7) hints that it behaves like another well-known effect, low beta (or low volatility).


Following is a summary of the authors’ findings on the “academic” quality measures:

  • Each of the academic characteristics has positive returns that are statistically significantly: 3.6% (with a t-statistic of 2.5) for gross profitability, 3.7% (with a t-statistic of 3.0) for accruals and 4.0% (with a t-statistic of 2.3) for net stock issues. The combined “academic” quality definition clearly benefits from diversification, as it has better performance (a premium of 6.2% with t-statistic of 4.2) than each individual characteristic with comparable volatility.
  • The “academic” definitions remain strong after accounting for exposure to other risk factors, as each individual factor has a highly significant alpha.
  • An investor can also achieve performance improvement by diversifying across multiple quality signals. The “academic” quality strategy has an alpha of 6.9% (t-statistic of 6.26), substantially higher than gross profits, accruals or net stock issues alone.
  • The academic quality factor is superior to the industry one.

These findings were confirmed in the data for non-U.S. developed as well as emerging markets. Whether looking at raw returns or at risk-adjusted alphas, “academic” consistently outperforms “industry.”


Quality In Bonds
As a further test of the quality premium’s robustness, the authors examined whether it existed in corporate bonds. Their corporate bond data set was constructed on the Barclays U.S. Corporate Investment Grade Index and the Barclays U.S. Corporate High Yield Index over the period January 1994 through December 2014. They only included bonds for companies with publicly traded equity due to the availability of accounting information.

In the case of multiple bonds outstanding, they include only one, preferring 1) senior bonds over subordinated ones; 2) bonds in the maturity segment of five to 15 years; 3) younger bonds; and 4) larger bonds. The final sample consists of 414 investment-grade bonds and 474 high-yield bonds.

The authors based their corporate bond analysis on returns in excess of duration-matched Treasuries, allowing them to focus on the default premium component of corporate bond returns, ignoring the term premium (which can be gained by investing in government bonds). Due to the lower liquidity of corporate bonds compared to equities, they chose a 12-month holding period, versus the one-month period used for stocks. Following is a summary of their findings:

  • The top portfolios of investment-grade bonds based on “industry” and “academic” quality definitions outperform the market in terms of excess return as well as on a risk-adjusted basis (with Sharpe ratios of 0.18 and 0.17 compared with 0.11 for the market), showing evidence for a quality premium.
  • The results for high-yield bonds show strong evidence that an investment strategy based on quality can also be profitably applied in corporate bond markets. Further, the superiority of the “academic” definition proves robust once again, with a top-minus-bottom premium of 3.1% (and a t-statistic of 2.51) compared with 0.6% (and a t-statistic of 0.4) for the “industry” definition.
  • A closer examination of the risk and return profiles for the top- and bottom-quality portfolios hints that investing in high-quality bonds effectively lowers the risk of default and earns a return premium.

The authors offered this important observation: “Perhaps the most striking take-away from this study is that there are large discrepancies between the stock quality measures used in academic studies and in the industry. Not only in terms of definitions that are used, but also in terms of the predictive value they have for future stock returns.”

This seems to be a case where the mutual fund industry, in aggregate, is ignoring the academic evidence. Given the efficiency of the market, it seems unlikely that this “ignorance” will continue, at least to the degree observed in this study. In the meantime, investors are best served by investing in funds that follow the evidence found in the academic literature.

Larry Swedroe is the director of research for The BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.


Larry Swedroe is a principal and the director of research for Buckingham Strategic Wealth, an independent member of the BAM Alliance. Previously, he was vice chairman of Prudential Home Mortgage.