Where’s the Beef?

In the 1980s there was a famous TV ad for Wendy’s with the tagline “Where’s the beef?”1 Many investors in today’s so-called smart beta strategies may well be asking a similar question, “Where’s the alpha?” Investors frequently buy into historical simulations or backtests, often supported by compelling studies by respected academics, suggesting wonderful performance with remarkable consistency, only to earn no alpha once they invest. The only winners typically are the asset managers and brokers through their fees and commissions. The problem is data mining and performance chasing, the nemeses of all investors. Yes, academics, “quants,” and investment professionals are all subject to those same temptations, very nearly to the same extent as retail investors. This article explores the ways seasoned professionals fall prey to these simple blunders and suggests the three lessons that could perhaps allow us to better meet client expectations, both by delivering improved outcomes and by encouraging more sensible expectations.

Key Findings

▪ Performance chasing and data mining are blunders afflicting traditional and quantitative investors of today’s so-called smart beta strategies.

▪ p-hacking, noise trading, fad chasing, and nowcasting are practices harbored within academia and the investment industry that exacerbate our innate tendency to embrace performance chasing and data mining.

▪ Assessing the impact of revaluation alpha, acknowledging the effect of implementation costs and other hidden costs, and addressing clients’ vulnerability to a performance expectations shortfall can lead to better investor outcomes.

Arguably the two greatest mistakes in investing are performance chasing and data mining. The two are interrelated, and quantitative investors (“quants”), reliant on computer models for their investment decisions, are no less prone to those errors than are traditional investors. We all are familiar with the SEC Rule 156 performance disclaimer that requires some variant of “past performance is no guarantee of future results.” And yet, human nature pulls us in the opposite direction. Any newly expensive asset, priced to disappoint in the future, likely got there by providing investors with joy and profit. It is painful to contemplate selling such assets. Reciprocally, any bargain likely got there by inflicting pain and losses. It goes against human nature to say, “I want more of that!”

In the 1980s, Barr Rosenberg, one of the great first-generation quants, was asked what advantage quantitative investors have over seasoned professionals, who carefully analyze the business prospects and relative values of individual companies. He famously quipped, “About 4% a year.” This is self-evidently no longer true. Quants now compete against one another, each seeking an edge. Trading is facilitated by high-frequency traders and market makers—and those quants with a short-term focus—all applying quantitative models on intraday tick data. During the past 20 years, we’ve written repeatedly on topics that—we believe—can help our clients better achieve their goals. In this article, we will touch on a few of those topics.