@article{dff629d3abad48a78871dfa9f5ec3b22,
title = "Are disagreements agreeable? Evidence from information aggregation",
abstract = "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 in- and 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.",
keywords = "Disagreement, LASSO, Machine learning, PLS, Return predictability",
author = "Dashan Huang and Jiangyuan Li and Liyao Wang",
note = "Funding Information: We are grateful to G. William Schwert (the editor) and Seth Pruitt (the referee) for very insightful and helpful comments that improved the paper significantly. We thank Adem Atmaz, Suleyman Basak, Bong-Geun Choi, Liyuan Cui, Zhi Da, Stefano Giglio, Shiyang Huang, Tao Li, Weikai Li, Ye Li, Francis Longstaff, Sungjune Pyun, and Guofu Zhou as well as seminar and conference participants at St. Louis University, Washington University in St. Louis, 2018 Asian Bureau of Finance and Economic Research (ABFER) Annual Conference, 2018 AsianFA Annual Meeting, 2018 City University of Hong Kong International Finance Conference on Corporate Finance and Financial Markets, 2018 Research in Behavioral Finance Conference, and 2018 SMU Finance Summer Camp for insightful comments. Dashan Huang acknowledges that this study was partially funded at the Singapore Management University through a research grant (MSS18B004) from the Ministry of Education Academic Research Fund Tier 1. Publisher copyright: {\textcopyright} 2021 Elsevier B.V. All rights reserved.",
year = "2021",
month = jul,
doi = "10.1016/j.jfineco.2021.02.006",
language = "English",
volume = "141",
pages = "83--101",
journal = "Journal of Financial Economics",
issn = "0304-405X",
publisher = "Elsevier B.V.",
number = "1",
}