Quality Conundrum, Part III – The Persistence and Performance of Quality

•     Introduction: How to Define Quality?

•     Quality Persistence and Kailash’s Simple Quality Proxy

o     Return on Equity

o     Accruals

o     EBITDA Margins

•     Quality Performance

•     Conclusion: Kailash Core Models Work Best

Introduction: How to Define Quality?

In discussions with clients, we have found that while most investors feel like they know what quality is, reaching any sort of consensus on the optimal definition of quality can be elusive. When describing quality firms, investors like to talk about high and consistent profitability, healthy balance sheets, ample free cash flow (FCF), capable and trustworthy management teams, efficient capital allocation and robust economic models which benefit from durable competitive moats. However, the real difficulty emerges when trying to translate these concepts around quality from qualitative statements into dispassionate constructs that can be tested in an empirically robust manner. In many ways, fundamental practitioners seem to (knowingly or unknowingly) perceive “quality” in a manner similar to Supreme Court Justice Potter Stewart’s definition of obscenity who famously quipped, “I know it when I see it.”¹ There are several dimensions of quality including management quality, balance sheet quality and earnings quality, among others, that may or may not be linked to each other. While these types of vague heuristics are common in many areas of equity research, we prefer to work with clearly defined metrics and then use history to test their validity and efficacy in predicting excess returns. Trying to convert general impressions of quality into “pure” quality metrics to help identify high quality stocks that will outperform and low quality stocks that will underperform within a future time period further complicates the debate on the optimal definition of quality.

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Several prominent asset management firms have provided their general ideas of quality, their own optimal quality metrics and have even created investment products based on these metrics. These firms’ quality constructs range from simple univariates to complex formulas that often comingle items which we would classify as value factors. For example, some investment companies use a simple metric of profitability such as return on equity (ROE) as the categorical way of defining “quality” while others can include any of the following: ROE, ROA, debt/equity, earnings variability, total profits/assets, gross margins, FCF yield, FCF/assets and annual changes in any of these metrics. Not only do the variables used vary, but the calculation methodology can vary as well. Many firms use equations with the coefficients based on regression analysis, but other methodologies are more complicated. For example, Piotroski’s F-Score² uses nine metrics to devise an integer scale from 0 to 9 in which one point is given for each of the metric conditions that are met: ROA > 0, operating cash flow (OCF) > 0, YoY growth in ROA > 0, OCF > net income (NI), lower long-term debt/assets than the prior year, higher current ratio than the prior year, no share count increase from equity issuance, higher gross margin and a higher asset turnover ratio than the prior year. The Piotroski’s F-Score was designed to be applied only to value stocks to determine which among the value stocks are the healthiest or have the highest quality. These few examples illustrate the wide diversity of approaches to the problem of optimally defining and measuring quality.

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