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- Publisher Website: 10.1016/j.jempfin.2023.04.001
- WOS: WOS:000985136700001
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Article: Cross-sectional uncertainty and expected stock returns
Title | Cross-sectional uncertainty and expected stock returns |
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Authors | |
Issue Date | 1-Apr-2023 |
Publisher | Elsevier |
Citation | Journal of Empirical Finance, 2023, v. 72, p. 321-340 How to Cite? |
Abstract | We study the predictability of cross-sectional uncertainty (CSU) for stock returns. We find that CSU exhibits significant power for predicting monthly stock returns both in and out of sample with annual R^2 of 11.89% and 6.34%, respectively, greater than popular predictors. A bivariate combination forecast using CSU with one of the alternative predictors yields annual out-of-sample R^2 up to 18.08%. CSU generates significant economic gains for a mean–variance investor with a utility gain of over 400 basis points per annum. A vector autoregression decomposition shows that the source of predictability mainly comes from a cash flow channel. |
Persistent Identifier | http://hdl.handle.net/10722/328277 |
ISSN | 2023 Impact Factor: 2.1 2023 SCImago Journal Rankings: 0.927 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Huang, D | - |
dc.contributor.author | Yu, D | - |
dc.date.accessioned | 2023-06-28T04:41:00Z | - |
dc.date.available | 2023-06-28T04:41:00Z | - |
dc.date.issued | 2023-04-01 | - |
dc.identifier.citation | Journal of Empirical Finance, 2023, v. 72, p. 321-340 | - |
dc.identifier.issn | 0927-5398 | - |
dc.identifier.uri | http://hdl.handle.net/10722/328277 | - |
dc.description.abstract | We study the predictability of cross-sectional uncertainty (CSU) for stock returns. We find that CSU exhibits significant power for predicting monthly stock returns both in and out of sample with annual R^2 of 11.89% and 6.34%, respectively, greater than popular predictors. A bivariate combination forecast using CSU with one of the alternative predictors yields annual out-of-sample R^2 up to 18.08%. CSU generates significant economic gains for a mean–variance investor with a utility gain of over 400 basis points per annum. A vector autoregression decomposition shows that the source of predictability mainly comes from a cash flow channel. | - |
dc.language | eng | - |
dc.publisher | Elsevier | - |
dc.relation.ispartof | Journal of Empirical Finance | - |
dc.title | Cross-sectional uncertainty and expected stock returns | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.jempfin.2023.04.001 | - |
dc.identifier.hkuros | 344847 | - |
dc.identifier.volume | 72 | - |
dc.identifier.spage | 321 | - |
dc.identifier.epage | 340 | - |
dc.identifier.eissn | 1879-1727 | - |
dc.identifier.isi | WOS:000985136700001 | - |
dc.identifier.issnl | 0927-5398 | - |