The authors of A Practitioner’s Defense of Return Predictability are Blair Hull, who founded Hull Trading Company in 1985 and served as the firm’s chairman and CEO before selling it to Goldman Sachs in 1999, and Xiao Qiao, who is a PhD candidate at the University of Chicago Booth School of Business.
I am very much in favour of practitioners coming together with academics to combine their knowledge and experience. Whereas academic papers often culminate in a t-statistic for a single idea, practitioners will often not be satisfied until they include a few time-series charts and spend some time thinking about implementation issues.
In this paper, they attempt to combine a vast array of ideas rather than focusing on one small marginal addition to the literature which, in my opinion, is one of the secret ingredients of quant investing. A single inefficiency might not be enough to convince the believers of market efficiency, but a combination of several insights can form a reliable trading strategy.
‘Combination’ is the key word here, in fact, in the conclusion:
Such an exercise will readily illustrate the importance of combining information in different return predictors. Another interesting extension is to examine alternate methods of combining forecasting variables.
More importantly, the paper claimed that there are several shortcomings to the current state of literature on return predictability:
Many studies examine return predictors in isolation. Some studies, such as Rapach, Strauss, and Zhou (2010), have attempted to combine information across predictors, but they only use a small set of predictors restricted to a similar time horizon. Instead, we look at a relatively large set of predictors, and combine them in sensible ways to produce better forecasts than they do separately. Previous studies often rely exclusively on ordinary least squares (OLS) in forecasting regressions. In contrast, we use correlation screening to filter out the least significant variables and combine predictors.
The current investor behavior is not only harmful to investors themselves, but also subjects the market as a whole to more risk than necessary. Trend-following behavior is destabilizing to the market, since investors’ buys and sells push the market further towards its extremes. When investors buy when the market is up, the price is bid up further and more investors buy. This causes large swings in price. On the other hand, contrarian behavior may be stabilizing in the sense that a contrarian investor would dampen swings in price since he sells when prices are high and buys when prices are low. TAA with market-timing is in the spirit of a contrarian strategy. When recent returns have been high, expected returns are likely to be low and a market-timing strategy would sell instead of buy. If a market-timing product that automatically buys when the market is low and expected returns are high were widely available for investors, its effect would be a stabilizing one for the market. All else equal, market volatility would be lower, and price swings would not be as large.
Having lived through two 50% plus peak to trough draw-downs in the past fifteen years, one should challenge the buy and hold mentality. The proponents of buy and hold may dismiss all efforts to time the market, saying it can’t be done, which this study disproves to some degree.
As our understanding of return predictability changes, so will the stigma associated with market-timing strategies. Anybody who claimed to implement a “market-timing” strategy in the past 30 years would have been considered irresponsible; as such a strategy was thought to underperform the buy-and-hold strategy. In the upcoming 30 years, it is likely that it will be considered irresponsible to not engage in informed market-timing. Investors should change their asset allocation as estimates for expected returns change, in order to maximize the longrun growth rate of their investment. In doing so, financial markets become more stable and less volatile.