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 ISSN 0893-9454.
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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: | Date Event February 2021 Published 25 May 2020 Available 8 April 2020 Accepted |
| Volume: | 34 |
| Number: | 2 |
| Page Range: | pp. 1046-1089 |
| DOI: | 10.1093/rfs/hhaa062 |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Re-use 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 |
| Date of first compliant deposit: | 5 May 2021 |
| Date of first compliant Open Access: | 25 May 2022 |
| Related URLs: | |
| URI: | https://wrap.warwick.ac.uk/151797/ |
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