Are Disagreements Agreeable? Evidence from Information Aggregation

60 Pages Posted: 4 Dec 2017 Last revised: 20 Jun 2019

See all articles by Dashan Huang

Dashan Huang

Singapore Management University - Lee Kong Chian School of Business

Jiangyuan Li

Singapore Management University, Lee Kong Chian School of Business, Students

Liyao Wang

Singapore Management University - School of Economics

Date Written: June 1, 2019

Abstract

Disagreement measures are known to predict cross-sectional stock returns but fail to predict market returns. This paper proposes a partial least squares (PLS) disagreement index by aggregating information across individual measures and shows that this index significantly predicts market returns both in- and out-of-sample. Alternative machine learning methods not only confirm the predictability of disagreement on market returns but also identify the most important disagreement measures. Consistent with Atmaz and Basak (2018), the predictability of our disagreement index is stronger in high sentiment periods, operates via a cash flow channel, and extends to market volatility and trading volume.

Keywords: Disagreement, Return predictability, PLS, PCA, LASSO, Machine learning

JEL Classification: G12, G14

Suggested Citation

Huang, Dashan and Li, Jiangyuan and Wang, Liyao, Are Disagreements Agreeable? Evidence from Information Aggregation (June 1, 2019). Asian Finance Association (AsianFA) 2018 Conference. Available at SSRN: https://ssrn.com/abstract=3077938 or http://dx.doi.org/10.2139/ssrn.3077938

Dashan Huang (Contact Author)

Singapore Management University - Lee Kong Chian School of Business ( email )

50 Stamford Road
Singapore, 178899
Singapore

HOME PAGE: http://dashanhuang.weebly.com/

Jiangyuan Li

Singapore Management University, Lee Kong Chian School of Business, Students ( email )

469 Bukit Timah Road
Singapore
Singapore

Liyao Wang

Singapore Management University - School of Economics ( email )

90 Stamford Road
Singapore, 178903
Singapore

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