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Macroeconomic Expectations and Expected Returns
Published online by Cambridge University Press: 01 April 2024
Abstract
Using the macroeconomic forecasts of professional economists, we construct a comprehensive macro condition index that summarizes subjective expectations of output, inflation, and labor and housing market conditions. The index predicts stock returns and produces countercyclical equity premium forecasts, both in- and out-of-sample. Our results contrast with the procyclical subjective equity premia documented in recent literature. We show that the index reflects the true but unobserved macroeconomic condition that impacts the equity premium. Moreover, the predictability is not affected by belief biases and operates via a discount rate channel. The index’s predictability conforms to an explanation based on time-varying risk premia.
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- © The Author(s), 2024. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington
Footnotes
We especially thank an anonymous referee and George Pennacchi (the editor), Jonas Eriksen, Jun Tu, and Hao Zhou, whose comments greatly improved the article. We also thank Jian Chen, Dashan Huang, Difang Huang, Fuwei Jiang, Gang Li, Kai Li, Weikai Li, Zilong Niu, Wolfgang Schadner, Guohao Tang, Yang You, Chu Zhang, Ran Zhang, Shen Zhao, and Guofu Zhou, as well as seminar participants at Southwestern University of Finance and Economics, Tianjin University, Xiamen University, the 2020 China International Risk Forum, the 2020 International Symposium on Financial System Engineering and Risk Management, the 2021 China Finance Scholar Forum, the 2021 Northern Finance Association Conference, the 2021 Financial Management Association Annual Meeting, the 2021 CUHK-Shenzhen Finance Workshop, the 2022 Asian Meeting of the Econometric Society in China, the 2022 Asian Finance Association Annual Meeting, and 2023 Financial Markets & Corporate Governance Conference for their helpful comments and suggestions. We thank Kenneth French, Amit Goyal, Dashan Huang, and Guofu Zhou for making their research data available online. All remaining errors are ours. Our research is supported by the Stable Support Plan Program of Shenzhen Natural Science Fund (Grant No. 20200925160401001), the Philosophy and Social Science Planning Project of Guangdong Province, China (Grant No. GD20XGL31), the Natural Science Foundation of Guangdong Province of China for Distinguished Young Scholars (Grant No. 2023B1515020045), and the National Natural Science Foundation of China (Grant No. 72371079).