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Bond risk premiums with machine learning

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Bianchi, Daniele, Büchner, Matthias and Tamoni, Andrea (2021) Bond risk premiums with machine learning. The Review of Financial Studies, 34 (2). pp. 1046-1089. doi:10.1093/rfs/hhaa062

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Official URL: http://dx.doi.org/10.1093/rfs/hhaa062

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Abstract

We show that machine learning methods, in particular, extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock- and labor-market-related variables are more relevant for short-term maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both channels.

Item Type: Journal Article
Subjects: H Social Sciences > HG Finance
Q Science > Q Science (General)
Divisions: Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH): Bonds -- Econometric models, Bonds -- Risk assessment, Machine learning , Bonds -- Forecasting -- Data processing
Journal or Publication Title: The Review of Financial Studies
Publisher: Oxford University Press
ISSN: 0893-9454
Official Date: February 2021
Dates:
DateEvent
February 2021Published
25 May 2020Available
8 April 2020Accepted
Volume: 34
Number: 2
Page Range: pp. 1046-1089
DOI: 10.1093/rfs/hhaa062
Status: Peer Reviewed
Publication Status: Published
Publisher Statement: This is a pre-copyedited, author-produced version of an article accepted for publication in The Review of Financial Studies following peer review. The version of record Daniele Bianchi, Matthias Büchner, Andrea Tamoni, Bond Risk Premiums with Machine Learning, The Review of Financial Studies, Volume 34, Issue 2, February 2021, Pages 1046–1089, is available online at: https://doi.org/10.1093/rfs/hhaa062
Access rights to Published version: Restricted or Subscription Access
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