@article{681554298d90423bb864595c55ab1a1d,
title = "Investor sentiment and paradigm shifts in equity return forecasting",
abstract = "This study investigates the impact of investor sentiment on excess equity return forecasting. A high (low) investor sentiment may weaken the connection between fundamental economic (behavioral-based nonfundamental) predictors and market returns. We find that although fundamental variables can be strong predictors when sentiment is low, they tend to lose their predictive power when investor sentiment is high. Nonfundamental predictors perform well during high-sentiment periods while their predictive ability deteriorates when investor sentiment is low. These paradigm shifts in equity return forecasting provide a key to understanding and resolving the lack of predictive power for both fundamental and nonfundamental variables debated in recent studies.",
keywords = "behavioral biases, economic predictors, non-fundamental predictors, regime-switching, return predictability",
author = "Liya Chu and Xuezhong He and Kai Li and Jun Tu",
note = "Funding Information: Financial support from the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 grant for J. Tu, the Australian Research Council under the Discovery Grants [DP130103210 and DE180100649] for X-Z. He and K. Li, and a discovery research grant supported by the Fundamental Research Funds for the Central Universities for L. Chu is gratefully acknowledged. The authors thank David Simchi-Levi (editor-in-chief), an associate editor, two anonymous referees, Petra Andrlikova (discussant), Doron Avramov, Zhanhui Chen (discussant), Lauren H. Cohen, Phil Dybvig, Hai Lin, Bing Han, Dashan Huang, Jennifer Huang, Robert Kimmel (discussant), Weiping Li, Hong Liu, Terry Pan, David Rapach, Avanidhar Subrahmanyam, Allan Timmermann, Rossen Valkanov, Changyun Wang, Jianfeng Yu (discussant), Guofu Zhou; seminar participants at Central University of Finance and Economics, Cheung Kong Graduate School of Business, Harbin Institute of Technology, Peking University Guanghua School of Management, Peking University HSBC Business School, Renmin University of China, Shanghai Jiao Tong University, Shenzhen University, Southwestern University of Finance and Economics, St. Louis University, Tsinghua University, Washington University, Wuhan University, Xiamen University, Zhongnan University of Economics and Law, University of International Business and Economics; and participants at the 2015 Singapore Management University (SMU) Finance Summer Camp, the 2015 Australasian Finance and Banking Conference, the 2016 Conference on Financial Predictability and Data Science, the 2016 China International Conference in Finance, the 2016 Financial Management Association Annual Meetings, the 2018 European Meeting of the Econometric Society, and the 2019 Asian Meeting of the Econometric Society for their very helpful comments. The authors also thank Jeffrey Wurgler and Sydney C. Ludvigson for kindly sharing their data online. An earlier version of this paper was prepared while K. Li was visiting SMU, whose hospitality he gratefully acknowledges. Liya Chu and Kai Li contributed equally to the article. Funding Information: History: Accepted by David Simchi-Levi, finance. Funding: Financial support from the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 grant for J. Tu, the Australian Research Council under the Discovery Grants [DP130103210 and DE180100649] for X-Z. He and K. Li, and a discovery research grant supported by the Fundamental Research Funds for the Central Universities for L. Chu is gratefully acknowledged. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/ mnsc.2020.3834. Publisher Copyright: Copyright: {\textcopyright} 2022 INFORMS",
year = "2022",
month = jun,
doi = "doi:10.1287/mnsc.2020.3834",
language = "English",
volume = "68",
pages = "4301--4325",
journal = "Management Science",
issn = "0025-1909",
number = "6",
}