Manager Sentiment and Stock Returns

Xiumin Martin is Professor of Accounting at the Washington University in St. Louis Olin Business School. This post is based on a recent article, forthcoming in the Journal of Financial Economics, by Professor Martin; Fuwei Jiang, Associate Professor of Finance at the Central University of Finance and Economics; Joshua Lee, Assistant Professor at the University of Georgia J.M. Tull School of Accounting; and Guofu Zhou, Frederick Bierman and James E. Spears Professor of Finance at the Washington University in St. Louis Olin Business School.

In this study, we investigate the asset pricing implications of manager sentiment, focusing on its predictability for future U.S. stock market returns. Intuitively, investors may simply follow managers’ sentiment in financial disclosures, even though this sentiment may not represent fully the underlying fundamentals of the firm. Hence, high manager sentiment may lead to speculative market overvaluations. When the true economic fundamentals are revealed to the market gradually, the misvaluation diminishes and stock prices reverse, yielding low future stock returns (Baker and Wurgler, 2007). However, it is an open empirical question whether such hypothesized effects are significant in the stock market.

We construct a manager sentiment index based on the aggregated textual tone in firm financial statements and conference calls. Using the standard dictionary method and the Loughran and McDonald (2011) financial and accounting dictionaries, we measure textual tone as the difference between the number of positive and negative words in the disclosure scaled by the total word count of disclosure. We then provide an aggregate index at a monthly frequency to gauge the overall manager sentiment in the market and investigate its impact on both aggregate and cross-sectional stock returns. We find that this first textual tone-based manager sentiment index significantly and negatively predicts future aggregate stock market returns. More specifically, the manager sentiment index yields a large in-sample R2 of 9.75%, and a one-standard deviation increase in manager sentiment is associated with a −1.26% decrease in the expected excess market return for the next month. In addition, the predictive power of manager sentiment is robust, with out-of-sample 8.38%. Hence, corporate managers as a whole tend to be overly optimistic when the economy and the market peak, and the manager sentiment index is a contrarian return predictor.

We also compare the return predictability of manager sentiment to various macroeconomic predictors. Specifically, we consider a set of 14 well-known macroeconomic variables used by Goyal and Welch (2008). We find that the predictive power of manager sentiment is greater than that of these other macroeconomic predictors, and remains largely unchanged after controlling for them.

We also examine the relationship between manager sentiment and subsequent aggregate earnings surprises to explore the cash flow expectation error channel. We find strong evidence that manager sentiment negatively predicts subsequent aggregate earnings surprises in the next year. In addition, we find that future information about aggregate earnings surprises helps to explain manager sentiment’s predictive power for future annual market returns. Our findings suggest that the expectation error for future cash flows is likely the primary force driving manager sentiment’s ability to predict future market returns.

We next examine the relationship between manager sentiment and future aggregate investment growth to explore the overinvestment channel. We find that periods with high manager sentiment are accompanied by high aggregate investment growth in the short run up to three quarters, but low subsequent aggregate investment growth in the long run up to two years. Our findings indicate that high manager sentiment captures managers’ overly optimistic beliefs about future returns to investment which leads to overinvestment.

We then compare the manager sentiment index with five existing measures of investor sentiment in the literature: 1) the Baker and Wurgler (2006) investor sentiment index; 2) the Huang, Jiang, Tu, and Zhou (2015) aligned investor sentiment index; 3) the University of Michigan consumer sentiment index; 4) the Conference Board consumer confidence index; and 5) the Da, Engelberg, and Gao (2015) Financial and Economic Attitudes Revealed by Search (FEARS) sentiment index. We find that the manager sentiment index correlates positively with all these existing investor sentiment measures. The largest correlation is with the Baker and Wurgler (2006) investor sentiment index at about 0.5. The other correlations are smaller, ranging from 0.1 to 0.2. However, we show that manager sentiment is significantly different from existing investor sentiment and it contains unique and incremental information.

Manager sentiment also negatively predicts the cross-section of stock returns, and the predictability is concentrated among stocks with high growth opportunities, high financial constraint, low dividend payout, high leverage, high financial distress, low profitability, high unexpected earnings, low price, high turnover, high beta, high idiosyncratic volatility, young age, and small market cap. These results suggest that stocks that are difficult to value and costly to arbitrage are more sensitive to manager sentiment-driven mispricing. In contrast, while investor sentiment could significantly forecast stocks that are costly to arbitrage, it cannot forecast those that are difficult to value.

Overall, our article proposes the first aggregate textual disclosure tone-based manager sentiment measure that contains unique sentiment information in predicting both the aggregate stock market and the cross section of stock returns, and its predictive power goes far beyond existing investor sentiment measures. Our results suggest that manager sentiment captures firm managers optimism reflected in their overinvestments, and that incorporating positive words helps predict stock returns in the aggregate time series and the effect of manager sentiment is particularly important for firms that are difficult to value and costly to arbitrage.

The complete article is available for download here.

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