Real-Time Macro Information and Bond Return Predictability: Does Deep Learning Help?

43 Pages Posted: 3 Feb 2020 Last revised: 18 May 2020

See all articles by Guanhao Feng

Guanhao Feng

City University of Hong Kong (CityUHK)

Andras Fulop

ESSEC Business School

Junye Li

ESSEC Business School

Date Written: May 1, 2020

Abstract

This paper examines whether deep/machine learning can help find any statistical and/or economic evidence of out-of-sample bond return predictability when real-time, instead of fully-revised, macro variables are taken as predictors. First, we find some statistical evidence for forecasting short-term non-overlapping excess bond returns using deep learning models. Second, for forecasting overlapping excess bond returns, more statistical evidence derives from using deep learning models and other machine learning models. However, all statistical evidence is much weaker than that found from using fully-revised macro data and generates minimal economic gains for a mean-variance investor, regardless of her level of risk aversion and whether she can take short positions.

Keywords: Deep Learning, Machine Learning, Bond Return Predictability, Real-Time Macro Data, Overlapping and Non-overlapping Returns

JEL Classification: C45, C53, G11, G12, G17

Suggested Citation

Feng, Guanhao and Fulop, Andras and Li, Junye, Real-Time Macro Information and Bond Return Predictability: Does Deep Learning Help? (May 1, 2020). Available at SSRN: https://ssrn.com/abstract=3517081 or http://dx.doi.org/10.2139/ssrn.3517081

Guanhao Feng (Contact Author)

City University of Hong Kong (CityUHK) ( email )

83 Tat Chee Avenue
Kowloon Tong
Hong Kong

Andras Fulop

ESSEC Business School ( email )

3 Avenue Bernard Hirsch
CS 50105 CERGY
CERGY, CERGY PONTOISE CEDEX 95021
France

HOME PAGE: http://www.andrasfulop.com

Junye Li

ESSEC Business School ( email )

5 Nepal Park
Singapore, Singapore 139408
Singapore

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