Earnings Forecasts and Price Efficiency after Earnings Realizations: Reduction in Information Asymmetry through Learning from Price

• 成果类型: Article
• 署名作者: Gong, Guojin; Qu, Hong; Tarrant, Ian
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University System of Georgia; Kennesaw State University; State University of New York (SUNY) System; University at Buffalo, SUNY
• 刊物名称: CONTEMPORARY ACCOUNTING RESEARCH
• ISSN/ISSBN: 0823-9150
• DOI: 10.1111/1911-3846.12615
• 发表日期: 2021
• 页码: 654-675
• 关键词: private information; MARKET-EFFICIENCY; aggregation; disclosure; cost; liquidity

摘要

When information asymmetry is a major market friction, earnings forecasts can lead to higher price efficiency even after the information in forecasts completely dissipates upon earnings realizations. We show this in an experimental market that features information asymmetry (i.e., some traders possess differential private information). Earnings forecasts reduce information asymmetry and lead to prices that reflect a greater amount of private information. Traders can learn more about others' information from prices. This information learned from past prices continues to reduce information asymmetry and improve price efficiency even after earnings realizations. We contribute to the disclosure literature by showing the evidence that the learning-from-price effect amplifies the impact of public disclosure on price efficiency.