Some of you are probably aware that I’m not the biggest fan of what academia teaches investors about the stock market and finance. But I had the pleasure recently of sitting in on my brother’s finance class at Fordham University, taught by Dr. Frank Werner.
During the class, Dr. Werner instructed students on the intuition behind the Capital Asset Pricing Model and, in general, did a darn good job of teaching (and my brother can’t say enough how great a teacher he is). But that’s not what grabbed my attention.
I spoke with Dr. Werner after the class about one of the (arguably many) problems of CAPM. I’ll try to avoid being too technical here, but for those familiar with CAPM, I believe there could exist omitted variable bias in a regression performed on a portfolio with the market. That is, while CAPM tries to determine the correlation of a stock with the market, the variance accounted for in the error term might be omitting the stock/portfolio’s effect on the market. In other words, factors “local” to a stock could actually have an impact on the market, meaning that a single variable regression just won’t do.
Think of it this way: say you take a portfolio of the 10 or 20 largest stocks. While CAPM tries to correlate these with the market, it may miss the point that something like Wal-Mart’s (NYSE:WMT) same-store sales or Exxon-Mobil’s (NYSE:XOM) profit margins or any host of local factors can actually have an impact on the market. This would lead to what statisticians call “omitted variable bias.” So what would need to be done to tweak CAPM is a multiple variable regression that “separates” these factors out and includes them along with the “macro factors” assumed to be independent of the stock(s).
This is certainly not a groundbreaking discovery on my part, and some folks have been running this sort of analysis for a while. Dr. Werner and I share an appreciation for the importance of looking at a business as a business and not a piece of paper driven by macro-factors. This means that the variables that should be looked at are those unique to the business rather than the market as a whole. CAPM just doesn’t cut it in this regard — so if academia insists on trying to find correlations and measuring risk as covariance, betas aren’t enough.
And what Dr. Werner is working on (and I believe he’s onto something important), is studying the less tangible factors, like a company’s adoption of green technologies, for instance, and how these affect performance. I personally believe we’re in the midst of a paradigm shift where companies who do things like treat employees very well, adopt environmentally friendly policies, “give back” to their communities, and invest in brand-building through social responsibility, will be rewarded with a bigger bottom line. Some investors may still balk at the idea of a company spending money on things that don’t seem to be an immediate value-add, but I can’t wait to start seeing some results in academia that demonstrate the quantitative benefits of these policies.
I applaud Dr. Werner and his colleagues who are working on improving the inherent limitations of Modern Portfolio Theory and I await the day when Wall Street and academia both drop their myopic views of stocks as ticker symbols with little more than betas and quarterly expectations. Okay, maybe that last part is wishful thinking, but you get my point.