Real-Time Macro Information and Bond Return Predictability: A Weighted Group Deep Learning Approach

60 Pages Posted: 3 Feb 2020 Last revised: 14 Dec 2022

See all articles by Yinghua Fan

Yinghua Fan

City University of Hong Kong (CityU)

Guanhao Feng

City University of Hong Kong (CityU)

Andras Fulop

ESSEC Business School

Junye Li

Fudan University - School of Management

Date Written: June 30, 2022

Abstract

Relying on a weighted group neural network model, this paper reexamines whether treasury bond returns are predictable when real-time, instead of fully-revised, macro information is used. Two types of real-time macro information are taken into account: macro vintage data and news-based topic attention. For non-overlapping bond returns, the evidence for predictability is weak. When forecasting overlapping bond returns, using only traditional macro variables, the neural network improves over standard machine learning tools for forecasting short-maturity bonds, but this predictability can hardly be translated to economic gains in the presence of realistic leverage constraints. In contrast, when adding textual factors to forecast overlapping returns, using the neural network becomes crucial and improves forecasting performance for all maturities. Adding news to the forecasters also increases economic gains when large leverage is allowed.

Keywords: Deep Learning, Bond Return Predictability, Real-Time Macro Data, News Topic Attentions.

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

Suggested Citation

Fan, Yinghua and Feng, Guanhao and Fulop, Andras and Li, Junye, Real-Time Macro Information and Bond Return Predictability: A Weighted Group Deep Learning Approach (June 30, 2022). Available at SSRN: https://ssrn.com/abstract=3517081 or http://dx.doi.org/10.2139/ssrn.3517081

Yinghua Fan

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Guanhao Feng (Contact Author)

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
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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