It has long been established that stocks with low variability in prices tend to outperform stocks with high variability. I’ve explored this in a few…
For the past few years, investors have noticed what we call a “value inversion,” which appears to be getting progressively worse. Theoretically—and normally—stocks with low price-to-sales ratios (cheap stocks) outperform those with high price-to-sales ratios (expensive stocks). Such was the case over the majority of the current century, and indeed, as James O’Shaughnessy has shown in What Works on Wall Street, for most of the twentieth century too.
When we as investors talk about volatility, we’re usually talking about variability in price returns. If an investment goes up and down 5% to 10% per day, that’s high volatility; if it goes up and down 0.05% to 0.1% per day, that’s low volatility. It’s a relatively simple concept, and is traditionally measured using standard deviation.
But when we compare investments to each other, we start talking not only about variability in price returns, but also about beta. And the implicit assumption is that beta measures something very different from variability.
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.
In high-volatility markets like the one we’re in now, low-volatility investing can offer considerable comfort. But it can also offer excess returns. In this article,…
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.
The conventional method of finding out whether or not a factor works is to look at the performance of the top (or bottom) ten or…