Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/239228 
Authors: 
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 13 [Issue:] 7 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-13
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
forecast efficiency
information friction
utility maximization
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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