When to Pick the Losers: Do Sentiment Indicators Improve Dynamic Asset Allocation?

Abstract

Recent finance research that draws on behavioral psychology suggests that investors systematically make errors in forming expectations about asset returns, and thus that investor sentiment can have predictive power for asset returns. A number of empirical studies using both market and survey data as proxies for investor sentiment have found support for these theories. In this study we investigate whether investor sentiment as measured by a component of the University of Michigan survey can help improve dynamic asset allocation over and above the improvement achieved based on commonly used business cycle indicators. We find that the addition of sentiment variables to business cycle indicators considerably improves the performance of dynamically managed portfolio strategies, both for a standard market-timer as well as for a momentum-type investor. Sentiment-based dynamic trading strategies, even out-of-sample, would not have incurred any significant losses during the October 1987 crash or the collapse of the `dot.com' bubble in late 2000. In contrast, standard business cycle indicators fail to predict these events, so that investors relying on these variables alone would have incurred significant losses. These strategies seem to systematically exploit investor over-reaction and are `active alpha' strategies with low betas and high alphas, in contrast to business cycle based strategies which are effectively `index-trackers' with high betas and considerably lower alphas.

Keywords: Investor Sentiment, Dynamic Asset Allocation

JEL Classification: C32, F39, G11, G12