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“In both stock and aggregate return predictions, there presumably exists a much larger set of predictors that were tested but failed to predict returns and were thus never reported.”
From Empirical Asset Pricing via Machine Learning (2020) · cites this paper as Green et al. (2013)
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Metadata
- Authors
- Green, J., Hand, J. R. M., & Zhang, X. F.
- Affiliations
- Pennsylvania State University (US)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Green, Jeremiah
- Hand, John R. M.
- Venue
- Review of Accounting Studies
- Published
- 2013-06-06
- Volume
- 18
- Issue
- 3
- Pages
- 692-730
- ISSN
- 1380-6653 (ISSN-L)
- 1573-7136
- OpenAlex
- W3123267500
- Cited by
- 219
- References
- 58
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 12 · 2023: 21 · 2022: 15 · 2021: 17 · 2020: 21 · 2019: 27 · 2018: 23 · 2017: 21 · 2016: 11 · 2015: 12 · 2014: 14 · 2013: 1
- FWCI
- 15.42
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Review of Accounting Studies (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- Funders
- City, University of London
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Business, Management and Accounting › Accounting › Financial Analysis and Corporate Governance
- Keywords
- stock returns, Sharpe ratio, orthogonalization, stock market efficiency, risk factors
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