Online Investor Sentiment via Machine Learning

Zongwu Cai and Pixion Chen
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Zongwu Cai: Department of Economics, The University of Kansas, Lawrence, KS 66045, USA
Pixion Chen: Division of Model Risk Management, Wells Fargo Bank, Charlotte, NC 28202, USA

No 202411, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio.

Keywords: Asset return; Machine learning; Nonlinearity; Portfolio allocations; Predictability. (search for similar items in EconPapers)
JEL-codes: C45 C55 C58 G11 G17 (search for similar items in EconPapers)
Date: 2024-09, Revised 2024-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
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