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A Few Notes on Quantitative Value

Posted in Fundamental Valuation, Value Premium

Wesley Gray (founder and executive managing member of Empiritrage LLC and Turnkey Analyst LLC) and Tobias Carlisle (founder and managing member of Eyquem Investment Management LLC) describe their 2013 book, Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors, as “first and foremost about value investment–treating stock as part ownership of a business valued through analysis of fundamental financial statement data. …There are several quantitative measures that lead to better performance…: enhancing the margin of safety, identifying the highest quality franchises, and finding the cheapest stocks. We canvass the research in each, test it in our own system, and then combine the best ideas in each category into a comprehensive quantitative value strategy.” Using price and fundamental data for a broad sample of U.S. stocks (over about 40 years ending with 2011) to confirm and refine key findings of value investing research streams, they argue and find that:

From Chapter 1, “The Paradox of Dumb Money” (Page 31): “The power of quantitative investing is in its relentless exploitation of edges. The objective nature of the quantitative process acts both as a shield and a sword. As a shield, it serves to protect us from our own cognitive biases. We can also use it as a sword to exploit behavioral errors made by others. …We seek to marry…quantitative approach to…value investment philosophy.”

From Chapter 2, “A Blueprint to Better Quantitative Value Strategy” (Page 56): “We increase the complexity in an effort to generate better results, but increased complexity is a double-edged sword. More steps in the model means more opportunities to make a mistake. How can we create a more complex investment process and expect to maintain discipline when investors have a hard time sticking to a simple strategy…? We…introduce the concept of a checklist, which is a simple way to break a necessarily complicated process into manageable pieces…”

From Chapter 3, “Hornswoggled! Eliminating Earnings Manipulators and Outright Frauds” (Page 78): “We have proposed three measures to identify financial statement manipulation. The first was scaled total accruals…, which sought to uncover earnings manipulation. …[The] bloated balance sheet phenomenon is measured by our second metric, scaled net operating assets… The final weapon in our arsenal is…a comprehensive predictive tool that identifies stocks with a high probability of fraud or manipulation.”

From Chapter 4, “Measuring the Risk of Financial Distress: How to Avoid the Sick Men of the Stock Market” (Page 89): “We use the PFD [probability of financial distress] when we wish to assess the probability that a stock will find itself in financial distress in the next 12 months. …The PFD can help investors avoid stocks at risk of sustaining a permanent loss of capital.”

From Chapter 5, “The Archetype of High Quality” (Page 111): “We propose several methods to deal with the high rate of mean reversion in return on capital. First, we seek stocks…that generate masses of cash after capital investments over an average business cycle. …[We] seek [either] stocks that have increased profit margins over a business cycle or stocks that have maintained high profit margins over a business cycle.”

From Chapter 6, “Financial Strength: Foundations Built on Rock” (Page 119): “…we have created a new financial strength score…, which we divide into the following three categories: Current profitability, Stability, Recent operational improvements.”

From Chapter 7, “Price Ratios: A Horse Race” (Pages 142-143): “The evidence is not conclusive, but the EBIT version of the enterprise yield [Earnings Before Interest and Taxes divided by Total Enterprise Value] seems to outperform the other price ratios on our full suite of analyses. It stands out in our analysis of raw performance. The portfolio created from the EBIT variation value decile generates a compound average growth rate of 14.55 percent per year…”

From Chapter 8, “Alternative Price Measures–Normalized Earning Power and Composite Ratios” (Page 162): “Our results are roughly consistent with the results from recent research…that long-term ratios add little to the predictive ability of single-year price ratios. …There is some weak evidence that composite price ratios outperformed on a rolling 5- and 10-year basis at the beginning of the sample, but little to indicate that any composite outperforms the single-year EBIT enterprise multiple [yield].”

From Chapter 9, “Blue Horseshoe Loves Anacott Steel: Follow the Signals from the Smart Money” (Page 182): “Buybacks, insider purchases, activist activity, and low SIRs [short interest ratios] indicate that the smart money may be excited about a stock. These signals combined with quantitative signals that a stock price is depressed below its valuation are very positive for future returns. …Stock issuance and high SIRs indicate that the smart money believes the stock is overvalued and ready for a tumble.”

From Chapter 10, “Bangladeshi Butter Production Predicts the S&P 500 Close” (Pages 206-208): “There are many ways that we can fool ourselves with investment simulations: statistical analyses can identify spurious relationships; we can inadvertently introduce look-ahead or survivorship biases by using the wrong database; we can overestimate the amount of capital the strategy can accommodate or the liquidity of the target stocks; or we can underestimate the transaction costs from frequent rebalancing. …we have sought to avoid the common pitfalls…”

From Chapter 11, “Problems with the Magic Formula” (Page 228): “The EBIT enterprise multiple [yield] has performed strongly throughout our tests. …There are some things that we can do, however, to goose the performance of the universe. …we found that removing from the universe even only a very small proportion of stocks that might be frauds, financial statement manipulators, or at high risk of financial distress improves the performance of the universe. We have also found separating the stocks remaining in the value decile into high and low quality, and then buying the high-quality stocks, leads to better performance.”

From Chapter 12, “Quantitative Value Beats the Market” (Page 257): “How does the performance of the Quantitative Value model portfolio compare to the performance of three of the top value investors [Sequoia Fund, Legg Mason Value Trust and Third Avenue Value Fund] over the past 20 years? …For this analysis, we apply a 1.5 percent management fee (paid monthly) and include trading and execution costs at 1 percent (paid monthly) in the Quantitative Value results. …The Quantitative Value strategy performs well in comparison with these legends of investing. It generated a better CAGR, at one of the lower standard and downside deviations, which led to excellent Sharpe and Sortino ratios.”

In summary, investors will likely find Quantitative Value a well-reasoned derivation of systematic value investing based on validation of well-cited streams of academic research.

Cautions regarding arguments/findings include:

  • As noted in the book (Chapter 10, page 206): “Unless we explicitly state otherwise, we report all returns throughout this book without fees and transaction costs. Our philosophy is that investors are better able to gauge the expected costs of running their own portfolio than we are.” The expected costs of running a portfolio today are generally much lower than those borne over much of the test period used in the book. See “Trading Frictions Over the Long Run” for a sense of the scale of trading frictions at different times and the difficulty of incorporating frictions into a long-run strategy test. It is plausible that the level of trading frictions affects gross strategy profitability, and that strategy variations have different turnovers and bear different trading frictions (such that the winners of gross and net contests may sometimes differ).
  • As argued in the book (Chapter 10), logical coherence/corroboration mitigates the data snooping bias associated with repeatedly searching a set of financial data (U.S. stock prices and fundamentals) for the investment strategy that works best. However, logical corroboration may not discriminate among close substitutes (such as stock valuation ratios), such that the best performing strategy tends to overstate out-of-sample performance. 
  • The Quantitative Value strategy is fairly complicated, such that the burdens of data collection/processing are material (or costly if delegated to a manager), as suggested in the “net” performance contest of Chapter 12.
  • The book, which focuses on U.S. stocks, does not address asset class diversification. The specific methods/metrics described may not apply to other asset classes, and data may not be available for other classes to support comparable testing.
  • As with most research on asset pricing, the book assumes that stock return distributions are tame enough to support a “normal” interpretation of distribution statistics. To the extent that actual distributions are wild, these interpretations break down. 
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