We are not the first to recognize this relationship between historical and realized betas. It has appeared in some prior academic research and clearly the team at Bloomberg recognized it. In all fairness, Bloomberg’s adjustment does appear to improve overall accuracy and we did not test its efficacy as a pricing model (in testing CAPM returns, for example). But it is surprising to us that traditional beta estimates are still widely used and interpreted the same way. To address the question from above, if the market went up 10% this quarter, the stock with a beta of 1.30 would probably not be up 30% more than the market (13%). In fact, a return closer to 15-20% greater would be more likely based the observed reversion of higher betas towards 1.0.
This built-in mean reversion in the traditional beta estimates raises some larger questions. There is a body of research supporting the existence of a “low beta anomaly” demonstrating that lower volatility stocks have both higher returns (and lower risk) in comparison to higher volatility stocks (see references below for more information). Yet according to the original portfolio theory dating back to Markowitz (1952), there is supposed to be a positive relationship between risk and return.
How much of this anomaly is driven by a fundamentally flawed estimate of future beta? If the traditional methods under-estimate low betas and over-estimate high betas, a strategy of buying the low beta and selling the high beta would be a way to profitably play the reversion to the mean (or towards 1.0 in this case). But if you had a better forecast of future beta, how does that change the dynamic?
This is exactly the question we looked to address in developing truBeta and founding Salt Financial. In future posts, we will delve into more of the details behind our process of using a more accurate forecast of beta to build portfolio construction tools.