New Online Investor Sentiment and Asset Returns
Zongwu Cai and
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Zongwu Cai: Department of Economics, The University of Kansas, Lawrence, KS 66045, USA
Pixiong Chen: Department of Economics, The University of Kansas, Lawrence, KS 66045, USA
No 202216, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics
This paper proposes two data-driven econometric approaches to construct online investor sentiment indices based on internet search queries, which are built by the partial least squares and LASSO methods, respectively. By examining the relationship between investor sentiment and stock risk premium on overall market level, our empirical findings are that these sentiment indices have predictive power both in and out of sample, and the out-of-sample predictability of the online investor sentiment indices proposed by the paper is robust for different horizons. Moreover, our investor sentiment indices are also able to predict the returns of cross-sectional characteristics portfolios. This predictability based on investor sentiment has economic value since it improves portfolio performance, in terms of certainty equivalent return gain and Sharpe ratio, for investors who conduct the optimal asset allocation.
Keywords: Asset return; Data-driven method; Online investor sentiment; Partial least squares; Portfolio choice. (search for similar items in EconPapers)
JEL-codes: C22 C53 G11 G17 (search for similar items in EconPapers)
Date: 2022-11, Revised 2022-11
New Economics Papers: this item is included in nep-big
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Persistent link: https://EconPapers.repec.org/RePEc:kan:wpaper:202216
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