Corrigendum: Bond Risk Premiums with Machine Learning

Daniele Bianchi; Matthias Büchner; Tobias Hoogteijling; Andrea Tamoni

doi:10.1093/rfs/hhaa098

Corrigendum: Bond Risk Premiums with Machine Learning

Daniele Bianchi, Matthias Büchner, Tobias Hoogteijling, Andrea Tamoni

Rutgers Business School, Finance & Economics

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

In this note we revisit the empirical results in Bianchi, Büchner, and Tamoni (2020) after correcting for using information not available at the time the forecast was made. Although we note a decrease in out-of-sample $R^2$, the revised analysis confirms that bond excess return predictability from neural networks remains statistically and economically significant.

Original language	English (US)
Pages (from-to)	1090-1103
Number of pages	14
Journal	Review of Financial Studies
Volume	34
Issue number	2
DOIs	https://doi.org/10.1093/rfs/hhaa098
State	Published - Feb 1 2021

All Science Journal Classification (ASJC) codes

Accounting
Finance
Economics and Econometrics

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10.1093/rfs/hhaa098

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title = "Corrigendum: Bond Risk Premiums with Machine Learning",

abstract = "In this note we revisit the empirical results in Bianchi, B{\"u}chner, and Tamoni (2020) after correcting for using information not available at the time the forecast was made. Although we note a decrease in out-of-sample $R^2$, the revised analysis confirms that bond excess return predictability from neural networks remains statistically and economically significant.",

author = "Daniele Bianchi and Matthias B{\"u}chner and Tobias Hoogteijling and Andrea Tamoni",

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AU - Büchner, Matthias

AU - Hoogteijling, Tobias

AU - Tamoni, Andrea

PY - 2021/2/1

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N2 - In this note we revisit the empirical results in Bianchi, Büchner, and Tamoni (2020) after correcting for using information not available at the time the forecast was made. Although we note a decrease in out-of-sample $R^2$, the revised analysis confirms that bond excess return predictability from neural networks remains statistically and economically significant.

AB - In this note we revisit the empirical results in Bianchi, Büchner, and Tamoni (2020) after correcting for using information not available at the time the forecast was made. Although we note a decrease in out-of-sample $R^2$, the revised analysis confirms that bond excess return predictability from neural networks remains statistically and economically significant.

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Corrigendum: Bond Risk Premiums with Machine Learning

Abstract

All Science Journal Classification (ASJC) codes

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