What Fama and French?s Latest Research Doesn?t Tell Us
With the high name recognition and respect that the team of Eugene Fama and Kenneth French enjoys in the world of finance, anything they publish warrants attention. Their latest offering, Size, Value, and Momentum in International Stock Returns, offers some interesting data on global equity performance. But they fail to offer any insights that explain the reasons behind their findings.
The duo of Fama and French is most famous for their 1992 and 1993 papers documenting strong historical value and size effects. (Fama is also famous – or infamous, depending on your perspective – for his association with the efficient market hypothesis.) The core observation of Fama and French’s seminal papers was that the returns on small-company and value stocks – those with high book-to-market value ratios – have historically outperformed the market to a greater degree than can be explained by the capital asset pricing model (CAPM).
I’ll go into this and their new findings in more detail a little later. First, I’ll offer a primer on their methodology. Then I’ll describe how they constructed their study, and the results they obtained. Finally, I’ll put on my curmudgeon hat and tell you what I think is wrong with the whole thing.
Fama and French’s latest paper is an exercise in running regressions. Most readers of Advisor Perspectives will be familiar with the term regression and to a degree with the method, but it’s worth a quick review to ensure that we all have the basic tools to assess their work.
Regression analysis is the most used mathematical modeling technique in all of social science, finance included – and the most overused. Regression is usually explained to students in about the second year of high school – at least it was in my high school. There, it was called least-squares fit.
You have data points that graph something like this:
You try drawing a straight line through them and measure its distance from each point, like this:
With a little math, you can find the line that minimizes the sum of the squares of those distances. This gives you the least-squares fit: