Equity and Bond Comovements: A Machine Learning Perspective

53 Pages Posted: 20 Dec 2023 Last revised: 29 Nov 2024

See all articles by Jiangyuan Li

Jiangyuan Li

Shanghai University of Finance and Economics

Liyao Wang

Hong Kong Baptist University

Jinqiang Yang

Shanghai University of Finance and Economics

Wei Zhou

Shanghai University of Finance and Economics

Date Written: October 31, 2023

Abstract

We study the comovements between stock and Treasury bonds from a machine learning perspective. Employing cutting-edge machine learning techniques and an extensive panel of characteristics, we assess the effectiveness of various machine learning models and identify the primary drivers of stock-Treasury correlation. All machine learning methods outperform traditional OLS regression, with dimension reduction techniques being the best-performing linear approaches and neural networks excelling among nonlinear methods. Stock illiquidity emerges as the most influential characteristic driving the negative stock-Treasury correlation, while two inflation-related measures are central to the positive stock-Treasury correlation. The positive stock Treasury correlation is closely tied to high inflation, whereas the subsequent negative correlation largely reflects a cross-market hedging dynamic.

Keywords: Stock-bond comovement, Machine Learning, Cross-market hedging

JEL Classification: G12, G17, P16, E44

Suggested Citation

Li, Jiangyuan and Wang, Liyao and Yang, Jinqiang and Zhou, Wei, Equity and Bond Comovements: A Machine Learning Perspective (October 31, 2023). Available at SSRN: https://ssrn.com/abstract=4655620 or http://dx.doi.org/10.2139/ssrn.4655620

Jiangyuan Li (Contact Author)

Shanghai University of Finance and Economics ( email )

777 Guoding Road
Shanghai, Shanghai 200433
China

Liyao Wang

Hong Kong Baptist University ( email )

Hong Kong

Jinqiang Yang

Shanghai University of Finance and Economics ( email )

777 Guoding Road
Shanghai, P.R.China, AK Shanghai 200433
China

Wei Zhou

Shanghai University of Finance and Economics ( email )

777 Guoding Road
Shanghai, Shanghai 200433
China

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