I’m going to go back through Greenbackd’s old posts to write a book on systematic deep value. I thought I’d foist my thoughts on some poor publisher somewhere and try to get the book published. Assuming that fails (and I am), watch for my self-published version from Booksurge. I enjoyed reading Paul Graham’s Hackers and Painters, which I understand is a collection of essays previously published on his site. That seems like a sensible approach to me. I’m going to write the chapters as essays for Greenbackd and hope that the poorer ones get some free editing from you fine folks. (I’d also appreciate it if you let me know if you think the whole thesis stinks.)
The central thesis for the book is quantitative (or systematic) value investment (either that or a collection of short stories in the style of Zachary Mason’s The Lost Books of the Odyssey, I haven’t yet decided). I’m primarily interested in three ideas:
1. The best performed metrics for assessing value in the real world. This is interesting to me for two reasons, and they are two sides of the same coin. First, many of the metrics that work in academia and in backtests run into some problems in the real world. In the immortal words of Yogi Berra (or Albert Einstein or Jan J.A. van de Snepscheut):
“In theory there is no difference between theory and practice. In practice there is.”This boils down to Jameses Montier and O’Shaughnessy versus the world of academia, most notably Lakonishok, Shleifer, and Vishny. For me, this is cognitive dissonance defined. Aswath Damodaran also has some interesting contributions on this topic. Second, some real-world received wisdom in value is no wisdom at all (or at least, that is what the data tell us). I’ve spent a great deal of time backtesting various value metrics. Some work; some make no difference at all; and some destroy value. Some great men of value investing promote these metrics, and that is why they persist. I’m not interested in “good” metrics, which I define as those that beat the market over the long run. I’m interested in optimal metrics; those that consistently beat the other metrics in the real world. I hope and assume that you are too.
2. The relative merits of quantitative and qualitative strategies in stock selection and portfolio construction. This is a justification for a systematic approach to investment. I believe that this particular event has been run and won by the quants, although my definition of “quant” hews more closely to Montier’s, O’Shaughnessy’s and Philip Tetlock’s definition than Scott Patterson’s. A good process-driven approach to long-only equity investment should and does provide a very good outcome. I think a good process is an austere Tetlockian algorithmic approach, which is in practice a screen that is very difficult to pass, but yields a high proportion of winners in the companies that do pass.Piotroski’s “F-Score” is an excellent example of such an algorithm (even though I don’t personally like it or use it for philosophical rather than practical reasons I’ll discuss at some later date). Assuming a company has a 50/50 chance of passing any one of the Piotroski F-Score algorithm’s 9 binary “gates,” only 1/(9^2) or about 1.23% will pass. When combined with a low decile price-to-book approach, the investable universe is very small indeed. In practice, only a handful pass the screen at any time, but those that do pass perform very well (see, for example, the Piotroski screen on the American Association of Individual Investors website).
3. The methods by which a portfolio can be made “robust.” This is perhaps the most esoteric of the subjects, and also the most interesting. There is a tension between portfolio theory suggested by the efficient markets hypothesis, real-world portfolio construction under the Kelly Criterion. I also think that Nassim Taleb’s thoughts on risk are useful to this discussion. The key to investing, as it is to many things, is to stay in the game. Once your stake is gone, there’s no way to come back (unless your investors don’t know the difference between the geometric and the arithmetic means, in which case just show them the geometric mean of your annual returns and party like it’s 1999). For this reason, I spend a lot of time agonizing about the ways that my fund can blow up. I’m not worried about the Rapture, the collapse of western civilisation, CERN’s Large Hadron Collider actually bringing a black hole into existence, or the Mesoamerican Long Count calendar being accurate. (I’m not worried because if any of these things occur we’ll likely have bigger problems than investment returns.) The Austrian economist in me is, however, worried about lots of other things. I think Nassim Taleb’s key insight is that we don’t need to any specific event to be foreseeable to know that we should be prepared for the occurrence of some event. History teaches us that the hundred-year storm rolls around much more frequently than its name would suggest.
While the central thesis is narrow, to do it justice the book will have to canvas a broad range of issues in behavioral and value investing. Some of the topics that are central to this project haven’t yet been written, and so they will be created in the interests of completing the project. The new format for Greenbackd means that I’ll be posting less frequently, but when I do post they’ll be longer chapter essay-style posts, and I’ll be avoiding (for the most part) current events. Thoughts and comments are most welcome.