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

40 Pages Posted: 3 Feb 2020 Last revised: 25 Jan 2021

See all articles by Guanhao Feng

Guanhao Feng

City University of Hong Kong (CityU)

Andras Fulop

ESSEC Business School

Junye Li

Fudan University - School of Management

Date Written: January 25, 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, when using pure real-time macro information alone, we find that deep learning cannot help find any statistical evidence for forecasting both non-overlapping and overlapping excess bond returns. In contrast, some machine learning models can help find some statistical evidence for forecasting overlapping excess bond returns. Second, when using both pure real-time macro information and yield curve information, we find that deep learning performs well for forecasting medium- and long-maturity overlapping excess bond returns, but such predictability is dominantly driven by yield curve information. Third, all statistical evidence of predictability 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? (January 25, 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 (CityU) ( 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

Fudan University - School of Management ( email )

No. 670, Guoshun Road
No.670 Guoshun Road
Shanghai, 200433
China

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