A few months ago three researchers published an astonishingly ambitious and compendious paper called “Is There a Replication Crisis in Finance?” (Their names are Theis…
Last year I created a screen on Portfolio123 that invests in companies in the Russell 3000 that spend heavily on R&D. (To access this screen,…
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…
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.
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.
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.
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…
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 Netflix Prize was a competition begun in 2006 to predict user ratings for films. Competitors were given ratings scrubbed of information about the users, and were challenged to find, using machine-learning methods, an algorithm based only on the raw data.