The theme emerging from the 2017 Advisory Panel presentations was really a defense of active management, a quite surprising outcome given that Research Affiliates is known as almost a quasi-index passive shop. I find that surprise is typically a good thing and can be a lot of fun, requiring that we rise to the challenge of processing the unexpected. Three main threads emerged from the presentations the attendees heard, all dealing with practitioners’ ever-present concern: alpha.
Thread #1: Alpha is a zero-sum game. This assertion generated some lively debate. If we agree alpha is measured by the amount of excess profit generated relative to the market, then it is mathematically true that for any investor with a billion dollars who has earned a 5% excess return relative to the market, a billion dollars that has lost 5% has to exist on the other side of the trade. What’s interesting about this observation is that we, as investors, really need to think about who exactly is consistently losing on the other side of the trade, and why they would keep playing a game they are so bad at.
By and large, retail investors, individual investors who are not very sophisticated, are in this losing position. Terry Odean walked us through a number of common human behaviors that help us understand how this continues to happen.
Joanne Hill’s take on the zero-sum game was slightly different. She acknowledged that, from a dollar-profit perspective, the excess return of the market ought to add to zero, but from a utility perspective, this is not necessarily true. You and I could both be happier—perhaps you having a bit more risk and return, and I having a little less of each. So, in my view, this is the main thread we should be thinking about: from a dollar-profit perspective, alpha is a zero sum, but from a utility perspective, risk sharing makes that zero-sum game look less dire.
Thread #2: What exactly is alpha? Posing this question, we enter the philosophical realm. The alpha of Thread #1 deals with the excess return generated versus the market portfolio, which is real dollars and cents. But academics talk about CAPM alpha, Fama–French alpha, alpha compared to a particular factor model, or perhaps even against a holdings-based analysis. How are these measures of alpha helpful? Are they a measure of skill? Or are they a noisy mismeasurement for which we lack an accurate interpretation?
We have come to learn that all sorts of reputed alphas exist because of all the different ways alpha can be measured. It might not actually be true that the presence of “alpha” means you should buy this or that product from this or that manager, which in the end makes all of these academic definitions of alpha not terribly productive.
Thread #3: Alpha and timing. Clearly, if you can time, you can generate alpha. The requirement then is that we need to know the source of the market-timing alpha. Many of our speakers talked about the two components of return: the transient, or mean-reverting, component and the persistent component, otherwise known as the random-walk component. If returns are not predictable—that is, if they are just random fluctuations, then prices are just random walks. Empirically, fundamental shocks to a firm’s cash flows and growth opportunities appear to be random and unpredictable. The discount rate applied to the cash flows does appear to be mean reverting, however, which makes returns predictable over appropriate horizons.
If you subscribe to that model, and I think the evidence suggests you should, then the corollary is that you might want to believe in mean reversion in asset classes, factor returns, and perhaps in manager performance. What this tells us is that a long-horizon investor should gain an advantage by simply ignoring short-term correlations and short-term spikes in volatility because these movements are related to transient shocks to the discount rate and will disperse over time.