The Role of Investor Sentiment and Valuation Uncertainty in the Changes around Analyst Recommendations: Evidence from U.S. Firms

Abstract

The authors investigate the empirical relation among investor sentiment, valuation uncertainty, and announcements of changes in analyst recommendation decisions among U.S. firms. Recent behavioral finance evidence shows market sentiment to have predictive content that affects the classical relationship between analyst recommendations and stock return dynamics. Contrary to this evidence, the authors find that degree of valuation uncertainty is associated to the impact of investor sentiment when examining a likelihood of consensus recommendation upgrade or downgrade. While not totally eliminating the significant investor sentiment effect under high valuation uncertainty, the investor sentiment does not powerfully explain the stock market reactions to analyst recommendation changes under low valuation uncertainty. Furthermore, the authors show that analyst recommendations provide significant buy or sell signals if valuation uncertainty is great, referring to the market being highly competitive. However, in less competitive markets, analyst reports become less informative. Overall, the authors demonstrate that magnitude of valuation uncertainty is an important complement to investor sentiment for further understanding analyst recommendations.

Keywords:

• Behavioral finance
• Investor sentiment
• Analyst stock recommendations
• Valuation uncertainty
• U.S. stock market

Notes

1 Informed investors are traders who tend to beat the market by using relevant information about the firm, while uninformed investors trade to liquidate their investments or balance their portfolios (Stigler, 1961). Often, informed traders trade differently from uninformed traders because they know whether the asset is over or undervalued. Massive trading on a particular set of stocks by informed investors cause rapid revelations of their information, resulting in the prices revealing all the private information in the early trades which could affect the sentiments of other investors (Holden and Subrahmanyam, 1992). In the financial markets, the well-informed agents can improve their profit by signaling their private information to the uninformed agents. In other words, the well-informed investors can give false signals to the uninformed agents in order to manipulate the uninformed investors (Spence, 1973; Bommel, 2003).

2 The series of (CCI) gathered by the Conference Board are published once every 2 months prior to June 1977. To be consistent with the frequency of other time-series (i.e., monthly frequency), the ‘missing values’ of these 2 indexes are filled with the latest observation until next observation became available following the procedure adopted by Lemmon and Portniaguina (2006) and Ho (2012).

3 Regarding the use of the 2 dummy variables (UP and DOWN) in this study, we want to report regression results of all dummies (i.e., Upgrade, and Downgrade) as they are important and following (K-1) formula, we are missing out one important dummy that is necessary to report. But we understand that we can't run regression with all dummies as it may create a constant of 1. In this case, the intercept constant term ${\beta }_0$ is dropped to avoid the dummy variable trap problem. Thus, our estimations are robust. We adopt this measure of omitting the intercept in the regression following Equations (8) and (9) respectively, in order to avoid dummy variable trap problem. The results are similar to those in Tables 4 and 5 and confirm our findings are consistent irrespective of the use of the constant term ${\beta }_0.$