Bond Risk Premia with Machine Learning

75 Pages Posted: 26 Aug 2018 Last revised: 19 Dec 2018

See all articles by Daniele Bianchi

Daniele Bianchi

University of Warwick - Finance Group

Matthias Büchner

University of Warwick - Warwick Business School

Andrea Tamoni

London School of Economics & Political Science (LSE)

Date Written: September 29, 2018

Abstract

We propose, compare, and evaluate a variety of machine learning methods for bond return predictability in the context of regression-based forecasting and contribute to a growing literature that aims to understand the usefulness of machine learning in empirical asset pricing. The main results show that non-linear methods can be highly useful for the out-of-sample prediction of bond excess returns compared to benchmarking data compression techniques such as linear principal component regressions. Also, the empirical evidence show that macroeconomic information has substantial incremental out-of-sample forecasting power for bond excess returns across maturities, especially when complex non-linear features are introduced via ensembled deep neural networks.

Keywords: Machine Learning, Deep Neural Networks, Forecasting, Bond Returns Predictability, Empirical Asset Pricing, Ensembled Networks.

JEL Classification: C38, C45, C53, E43, G12, G17

Suggested Citation

Bianchi, Daniele and Büchner, Matthias and Tamoni, Andrea, Bond Risk Premia with Machine Learning (September 29, 2018). Available at SSRN: https://ssrn.com/abstract=3232721 or http://dx.doi.org/10.2139/ssrn.3232721

Daniele Bianchi (Contact Author)

University of Warwick - Finance Group ( email )

Gibbet Hill Rd
Coventry, CV4 7AL
Great Britain

HOME PAGE: http://whitesphd.com/

Matthias Büchner

University of Warwick - Warwick Business School ( email )

Coventry CV4 7AL
United Kingdom

Andrea Tamoni

London School of Economics & Political Science (LSE) ( email )

Houghton Street
London, WC2A 2AE
United Kingdom
02079557303 (Phone)

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