Gray and Carlisle,Quantitative Value

I often despair about the inordinate number of hours I spend on this blog. Moreover, reading and writing about books that don’t engage me is a tedious project—and I really hate tedium. I don’t mind wasting time; I’m good at that. But I want to waste my time in a pleasurable way. I bitch and moan and frequently consider ditching the whole project when along comes a book that makes everything worthwhile. Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors by Wesley R. Gray and Tobias E. Carlisle (Wiley, 2013) is just such a book.

I guess serendipity must have been at work because Quantitative Value was an unlikely book to capture my imagination. I don’t consider myself a value investor and I’ve admitted more than once that I’m no quant. But this book is a brilliant synthesis not only of the philosophies of Warren Buffett and Ed Thorp but of recent literature on such themes as behavioral biases and checklists. The authors discuss these topics in a way that makes them seem fresh even to a seasoned reader.

Gray and Carlisle take the reader step by step through the process of building a quantitative value model, a process that I can’t possibly outline here—not because it’s overly mathematical (it isn’t) but because it can’t meaningfully be reduced to a few sentences. It is probably sufficient to say that they “don’t generate [their] ideas through statistical analysis and curve fitting.” No Bangladeshi butter production predicting the S&P 500 close for them. As they write, “We rely on tried-and-true security analysis techniques, and we supplement these metrics with academic research and common sense. We also tend toward simplicity in our measures where possible.” (p. 208)

Once the model is tested and shows outstanding past results, the investor must stick with the model even though “it often feels like one or more of the current crop of stocks selected by the model are particularly weak and should be avoided.” “Fiddling with the model’s output is an error that leads inexorably to underperformance.” They quote Jim Simons of Renaissance Technologies: “Did you like what the model said or did you not like what the model said? That is a hard thing to backtest. If you are going to trade using models, you just slavishly use the models; you do whatever the hell it says no matter how smart or dumb you think it might be at that moment.” (p. 249) Simons certainly has the track record to prove that his fund’s models were pretty darned smart.

And what about the authors’ model? How has it performed? Between 1974 and 2011 it generated a compound annual growth rate of 17.68% as opposed to the S&P 500 Total Return Index’s return of 10.46%, and it experienced lower volatility and a smaller worst drawdown.

Quantitative Value is to my mind a must-read book. It doesn’t matter whether you are a short-term trader, a trend follower, a technical analysis junkie, or an investor in search of a strategy. You will find something of value (pun intended) here.