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Open AccessArticle

Information Frictions and Stock Returns

Department of Accounting and Finance, Youngstown State University, University One Plaza, Youngstown, OH 44555, USA
J. Risk Financial Manag. 2020, 13(7), 140;
Received: 14 April 2020 / Revised: 22 May 2020 / Accepted: 27 May 2020 / Published: 1 July 2020
(This article belongs to the Special Issue Quantitative Risk)
The purpose of this paper is to assess the impact of ambiguity on financial analyst forecast incentives and the associated abnormal stock returns. I present a model incorporating ambiguity aversion into a two-period Lucas tree model. The resulting model confirms the role of ambiguity in the determination of asset returns. In particular, the model with ambiguity aversion generates a lower price and a higher required rate of returns compared to the classical model without ambiguity concern. I construct a measure of ambiguity and provide empirical evidence showing that the incentive of analysts to misrepresent information is a function of ambiguity. Analysts are more likely to bias their forecasts when it is more difficult for investors to detect their misrepresentation. Under ambiguity, analysts’ optimistic forecasts for good/bad news tend to deteriorate. Moreover, stock returns are positively related with ambiguity. Under ambiguity neither good nor bad news is credible. Investors systematically underreact to good news forecast and overreact to bad news forecast when ambiguity exists. View Full-Text
Keywords: information friction; utility maximization; forecast efficiency information friction; utility maximization; forecast efficiency
MDPI and ACS Style

Yang, X. Information Frictions and Stock Returns. J. Risk Financial Manag. 2020, 13, 140.

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