Tests of investor learning models using earnings innovations and implied volatilities

Thaddeus Neururer, George Papadakis and Edward J. Riedl ()
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Thaddeus Neururer: Boston University
George Papadakis: U.S. Securities and Exchange Commission
Edward J. Riedl: Boston University

Review of Accounting Studies, 2016, vol. 21, issue 2, No 2, 400-437

Abstract: Abstract This paper investigates alternative models of learning to explain changes in uncertainty surrounding earnings innovations. As a proxy for investor uncertainty, we use model-free implied volatilities; as a proxy for earnings innovations, representing signals of firm performance likely to drive investor perceptions of uncertainty, we use quarterly unexpected earnings benchmarked to the consensus forecast. We document that uncertainty declines on average after the release of quarterly earnings announcements and this decline is attenuated by the magnitude of the earnings innovation. This latter result is consistent with models that incorporate signal magnitude as a factor driving changes in uncertainty. Most important, we document that signals deviating sufficiently from expectations lead to net increases in uncertainty. Critically, this result suggests that models allowing for posterior variance to be greater than prior variance even after signal revelation [e.g., regime shifts in Pastor and Veronesi (Annu Rev Financ Econ 1:361–381, 2009)] better describe how investors incorporate new information.

Keywords: Uncertainty; Implied volatilities; Earnings innovations; Regime shifts; Bayesian learning (search for similar items in EconPapers)
JEL-codes: G13 G14 M41 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (18)

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DOI: 10.1007/s11142-015-9348-5

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