As Michael Mauboussin relates, not too long ago the Columbia Business School sent a group of students to meet with Todd Combs, the investment manager at Berkshire Hathaway and (currently) CEO of Geico. He recommended that they read 500 pages a day. The students were dumbfounded. Combs’s colleague at Berkshire, Vice Chairman Charlie Munger, has said, “In my whole life, I have known no wise people (over a broad subject matter area) who didn’t read all the time—none, zero.” And Warren Buffett himself has suggested that he devotes 80% of his working day to reading.
If you’re a quantitative investor or trader, you build a model and then backtest it to see if it has worked in the past; if you’re like most people, you try to improve your model with repeated backtests. You’re operating under the assumption that there will be at least some modest resemblance between what has worked in the past and what will work in the future. (If you didn’t assume that, you wouldn’t backtest at all.) But what few backtesters do after building their model is to try to break it by subjecting it to stress tests. A truly robust model should withstand every moderate attempt to break it.
Mauboussin writes, “success in investing has two parts: finding edge and fully taking advantage of it through proper position sizing. Almost all investment firms focus on edge, while position sizing generally gets much less attention.” This is because position sizing is a forbidding concept. If you try mean-variance portfolio optimization or using the Kelly criterion to decide how much to put into each stock you own, you’re likely to get bogged down in remarkably complex computations with results that are indefinite at best.
As a factor, momentum—the idea that a stock’s relative returns over the past six to twelve months have a tendency to persist over the next six to twelve months—has proved remarkably resilient. Academics first recognized this factor in the early 1990s, and its return premium has since been verified over the past 220 years (no, this is not a typo) of US equity data.
Mauboussin breaks down two functions of the price of a stock. First, it tells us (gives us information about) how much the market believes a stock is worth. Second, it acts as an influence upon buyers: if a price is rising, people want to get in on the rise and buy; if a price is falling, investors are more likely to want to sell. The task of a great investor is to learn how to separate the two, subscribe only to the information, and ignore the influence.
Let’s say two investors buy the same stock. Investor A paid $70 for it and investor B pail $100 for it. The stock is presently worth $85. Investor A will be happy and likely to think favorably of the stock because he made 21% on it; investor B will be sad and likely to think unfavorably of the stock because he lost 15% on it.