The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning

Turan G. Bali, Amit Goyal, Dashan Huang, Fuwei Jiang () and Quan Wen
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Turan G. Bali: Georgetown University - Robert Emmett McDonough School of Business
Dashan Huang: Singapore Management University - Lee Kong Chian School of Business
Quan Wen: Georgetown University - Department of Finance

No 20-110, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We provide a comprehensive study on the cross-sectional predictability of corporate bond returns using big data and machine learning. We examine whether a large set of equity and bond characteristics drive the expected returns on corporate bonds. Using either set of characteristics, we find that machine learning methods substantially improve the out-of-sample predictive power for bond returns, compared to the traditional linear regression models. While equity characteristics produce significant explanatory power for bond returns, their incremental predictive power relative to bond characteristics is economically and statistically insignificant. Bond characteristics provide as strong forecasting power for future equity returns as using equity characteristics alone. However, bond characteristics do not offer additional predictive power above and beyond equity characteristics when we combine both sets of predictors.

Keywords: machine learning; big data; corporate bond returns; cross-sectional return predictability (search for similar items in EconPapers)
JEL-codes: C13 G10 G11 (search for similar items in EconPapers)
Pages: 64 pages
Date: 2020-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk, nep-ore and nep-sea
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp20110

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