 Investor sentiment indices based on k-step PLS algorithm: A group of powerful predictors of stock market returnsZiyu Song and Changrui Yu International Review of Financial Analysis, 2022, vol. 83, issue C Abstract: We construct a group of new investor sentiment indices by applying a new dimension reduction technique called k-step algorithm which adopts partial least squares method recursively. With the purpose of forecasting the aggregate stock market return, the new group of investor sentiment indices performs a greater ability in predicting the market return than existing investor sentiment indices in and out of sample by adequately using the information in residuals and eliminating a common noise component in sentiment proxies. This group of new investor sentiment indices beats five widely used economic variables and still has a strong return predictability after controlling these variables. Moreover, they could also predict cross-sectional stock returns sorted by industry, size, value, and momentum and generate considerable economic value for a mean-variance investor. We find the predictability of this group of investor sentiment indices comes from its forecasting power for discount rates and market illiquidity. Keywords: Investor sentiment; Return predictability; Partial least squares; Residual (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:83:y:2022:i:c:s1057521922002733 DOI: 10.1016/j.irfa.2022.102321 Access Statistics for this article International Review of Financial Analysis is currently edited by B.M. Lucey More articles in International Review of Financial Analysis from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.