What They Don't Teach You in College about Investing
Updated: Apr 29, 2018
Finance and investment students are often taught beautiful and logical theories, but many don't stand the test of the real world.
Welcome to my first blog post here at Stone Investment Partners. Since it is almost time for the Berkshire Hathaway annual meeting and my first blog, I thought I would share one of my favorite papers from a few years ago. Back at the 2009 Berkshire Hathaway annual meeting, Warren Buffett said “There is so much that’s false and nutty in modern investing practice and modern investment banking, that if you just reduced the nonsense, that’s a goal you should reasonably hope for."
For a long time I thought about writing about the mean-variance (MV) optimization as an example of Buffett's comments. Many thanks to my co-authors Chen He, CFA and Paul White, PhD for helping bring this to life far beyond what I could do myself. While MV is still taught today, the blind use of MV without knowledge of its assumptions and limitations is a recipe for financial ruin. Amazingly from what I have seen personally, some investment professionals still use naïvely use MV.
Here are some of the considerations when using MV noted in our paper:
1) MV assumes that investment returns are normally distributed. Investment returns are rarely (if ever) normally distributed. In other words, "extreme" outcomes will happen more often than predicted by a normal distribution.
2) MV assumes that variance is a adequate measure of risk. It also treats upside and downside variations equally. I don't know about you, but I haven't had any clients complain about investments going up too much. In any case, there is no doubt that variance cannot capture all the risks in an asset. Investors worried about the risk of ruin need to consider other factors.
3) MV is extremely sensitive to model inputs. A small change is inputs can cause large changes in the "optimal" portfolio. In addition, MV often puts the vast majority of the portfolio in one asset.
4) Contrary to the model, higher risk (defined as volatility in the MV framework) does not positively correlate with higher returns in the real world. Research has shown that the positive relationship between risk (variance) and returns does not hold for the U.S. or international stocks.
George E. P. Box once said, “All models are wrong, but some are useful.” Harry Markowitz’s Mean-Variance (MV) framework is a foundation of Modern Portfolio Theory (MPT) is a great addition to the way we think about diversification and remains useful. The point of the paper is that "naïve usage of the MV or likely any other optimization would almost certainly lead to biased and fragile asset allocation solutions with unanticipated risks." In Your Money & Your Brain, author Jason Zweig recounts a story about Harry Markowitz regarding MV. Markowitz said, “I should have computed the historical covariances of the asset classes and drawn an efficient frontier, but I visualized my grief if the stock market went way up and I wasn’t in it—or if it went way down and I was completely in it. So I split my contributions fifty-fifty between stocks and bonds.”
As noted in the paper, "portfolios optimized to certain input forecasts are by definition more fragile since any divergence from that forecast would propagate through the optimization process and corrupt the output." Though the solution is technical in nature, the paper also shows the fragility of using normal distributions in optimizations by building on the work of Nassim Taleb. Focus should be on a well-defined portfolio construction process and the limitations/assumptions inherent in any tool used in the construction by the investor or their investment advisor. Click here for the full paper.