Are disagreements agreeable? Evidence from information aggregation *

• 成果类型: Article
• 署名作者: Huang, Dashan; Li, Jiangyuan; Wang, Liyao
• 署名单位: Singapore Management University; Shanghai University of Finance & Economics; Hong Kong Baptist University
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2021.02.006
• 发表日期: 2021
• 页码: 83-101
• 关键词: Disagreement; Return predictability; PLS; Lasso; Machine Learning

摘要

Disagreement measures are known to predict cross-sectional stock returns but fail to predict market returns. This paper proposes a partial least squares disagreement index by aggregating information across individual disagreement measures and shows that this index significantly predicts market returns both inand out-of-sample. Consistent with the theory in Atmaz and Basak (2018), the disagreement index asymmetrically predicts market returns with greater power in high-sentiment periods, is positively associated with investor expectations of market returns, predicts market returns through a cash flow channel, and can explain the positive volume-volatility relationship. (c) 2021 Elsevier B.V. All rights reserved.