Financial statement analysis and security valuation are a big part of our training as CFA charterholders; for many of us, they are the most important part. We pride ourselves on the thoroughness, sophistication, and level of detail of our analysis. A friend of mine in private equity explained that his due diligence process for buying a company was so thorough it included learning “the first name of the CEO’s second mistress.” But how much impact does that particular detail have on investment performance? How many of the myriad qualitative facts that we gather about a company ahead of an investment decision actually make a difference?
Wesley Gray and Tobias Carlisle offer a different approach in their fascinating book Quantitative Value. They begin with two simple observations. First, value stocks — as a group and over the long term — outperform growth stocks. Second, value managers — also as a group — underperform simple computer-generated value benchmarks, such as the magic formula1, which seeks to buy cheap stocks of high quality. This formula ranks the universe of stocks on two metrics: cheapness (Earnings before interest and taxes/Enterprise value, or EBIT/EV) and quality (EBIT/[net fixed assets + working capital]). Stocks with the best combined ranks (cheapest and highest quality) are selected for the portfolio. Quality, the second observation, is particularly inconvenient for me, as a value manager. My fees would be easier to justify if simple models, such as the magic formula, performed a bit worse, or better yet, did not exist.
As it turns out, we asset managers are not alone. In field after field, from psychology assessments to horse racing, human experts underperform simple automated decision models, even when the experts have access to the output from those models. Why would that be? The authors blame behavioral biases that we all suffer from and suggest that the art of security analysis should really be more of a science. Their goal is to improve on the magic formula, which they argue is one of the simplest and most effective value frameworks available.
To that end, Quantitative Value is organized as an investment checklist: Part 2 seeks to identify and avoid frauds and financially distressed stocks. Part 3 seeks to quantify and rank stocks by quality. Part 4 does the same for price. Part 5 seeks “corroborative signals,” such as stock buybacks and insider selling. Part 6 puts the findings together into a comprehensive model. The authors claim that their model outperformed the magic formula by 3.74% per year from 1974 through 2011 and had a better Sharpe ratio (0.74 versus 0.55) and a slightly lower maximum drawdown. This seems like a modest edge, but it compounds to a massive advantage over the 37-year time period studied.
The book’s greatest strength lies in its systematic and objective approach to a field dominated by mystique and high-profile personalities. As security analysts, we are used to learning from anecdotes and case studies. It is refreshing to see our craft subjected to an unflinching, scientific line of inquiry; the book seeks to draw conclusions not from individual cases (although these are provided) but from what works best over the full set of investment choices in the last 37 years. For example, the authors found that the magic formula derives all of its “magic” from the cheapness metric (EBIT/EV) and none from its quality metric (EBIT/[net fixed assets + working capital]). According to Gray and Carlisle, a portfolio of stocks sorted only on the cheapness metric achieves an astounding return of 15.95% a year and outperforms the two-metric magic formula by more than 2% per year. This finding alone, if true, should be an eye opener for most practitioners. How many of us have achieved a better return over any length of time? How many of our favorite financial ratios and analysis techniques truly add value? In other words, if each of our favorite metrics were consistently applied to select securities over several decades, would it produce a superior, or even adequate, portfolio return? If we don’t know the answer, what place do these metrics have in our investment process?
It is thus somewhat disappointing that Quantitative Value does not apply the same Occam’s razor approach to its own checklist, which runs to five pages on my e-book reader. Some parts of this checklist add considerably more value than others. As mentioned earlier, stocks selected with a single metric (EBIT/EV) can generate a return of 15.95% a year. The full model, which uses dozens of metrics and sub-models, generates a return of 17.68% a year. It seems like a high price to pay, in terms of added complexity, for an extra 1.73 percentage points a year. Perhaps if the authors were as critical of their checklist as of the magic formula the book would be quite a bit shorter.
Editor’s note: For More on this book, read Dennis McLeavy’s review here
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