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…
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.
In this piece, I want to drill down deep into a ratio that I find useful in assessing potential investments: the ratio of net operating assets (NOA) to total assets (the lower, the better). It’s my favorite balance sheet ratio.
Alpha has, over the last fifty years, become the standard way to measure the active return of a portfolio: how much the portfolio outperformed the benchmark. Technically, however, it’s a point on a line, specifically the point where the line crosses the y axis.
The other night I asked my son, a high school student who knows next to nothing about economics or the stock market, “Which is more likely to grow faster, a small company or a large company?” He answered, “A small company.” Then I asked him, “Which will rise in price faster, a company whose price is cheap compared to what it earns, or a company whose price is expensive?” He answered, “The cheap one.”
Mauboussin writes, “Great investors do two things that most of us do not. They seek information or views that are different than their own and they update their beliefs when the evidence suggests they should. Neither task is easy. . . . The best investors among us recognize that the world changes constantly and that all of the views that we hold are tenuous. They actively seek varied points of view and update their beliefs as new information dictates. The consequence of updated views can be action: changing a portfolio stance or weightings within a portfolio.”