The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.
Bond Risk Premiums with Machine Learning
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.
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Cited by
Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability
Avramov, D., Cheng, S., & Metzker, L. (2023) · Management Science · doi:10.1287/mnsc.2022.4449 · Cited by 217
Machine Learning Versus Economic Restrictions: Evidence from Stock Return Predictability
Avramov, D., Cheng, S., & Metzker, L. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3450322 · Cited by 74
Macro, Finance, and Macro Finance: Solving Nonlinear Models in Continuous Time with Machine Learning
Duarte, V. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3012602 · Cited by 9
Empirical Asset Pricing via Machine Learning: The Role of Research Design Choices
Lalwani, V., Meshram, V., & Jindal, V. (2026) · European Financial Management · doi:10.1111/eufm.70033
Metadata
- Authors
- Bianchi, D., Büchner, M., & Tamoni, A.
- Venue
- The Review of Financial Studies
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-05-25
- Volume
- 34
- Issue
- 2
- Pages
- 1046-1089
- ISSN
- 0893-9454 (ISSN-L)
- 1465-7368
- OpenAlex
- W3028931246
- Cited by
- 409
- References
- 97
- FWCI
- 41.12
- Type
- journal article
- Language
- en
- Open access
- closed
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