Integration of Macroeconomic Data into Multi-Asset Allocation with Machine Learning Techniques

25 Pages Posted: 27 May 2020

See all articles by Amine Abouseir

Amine Abouseir

CentraleSupélec

Arthur Le Manach

CentraleSupélec

Mohamed El Mennaoui

University of Paris-Saclay - CentraleSupélec

Ban Zheng

Ecole Polytechnique; HSBC Global Asset Management

Date Written: April 27, 2020

Abstract

In this paper, we propose a new way to predict market returns for multi-assets (equity, fixed-income and commodity) by extracting features from macroeconomic data and performing machine learning algorithms for both regression and classification. Our approach aims to select robust models to build alternative risk premia portfolio. We apply machine learning algorithms to our investment universe and then apply different portfolio allocation methods. We discover the importance of integrating macroeconomic data to build portfolio, especially with classification techniques which enhance the Sharpe ratios of strategies.

Keywords: risk premia, macroeconomic data, machine learning, portfolio allocation, regression, classification.

JEL Classification: C50, C60, G11.

Suggested Citation

Abouseir, Amine and Le Manach, Arthur and El Mennaoui, Mohamed and Zheng, Ban, Integration of Macroeconomic Data into Multi-Asset Allocation with Machine Learning Techniques (April 27, 2020). Available at SSRN: https://ssrn.com/abstract=3586040 or http://dx.doi.org/10.2139/ssrn.3586040

Amine Abouseir

CentraleSupélec ( email )

Labo M.A.S
Grande Voie des Vignes
Châtenay-Malabry CEDEX, 92295
France

Arthur Le Manach

CentraleSupélec ( email )

Labo M.A.S
Grande Voie des Vignes
Châtenay-Malabry CEDEX, 92295
France

Mohamed El Mennaoui

University of Paris-Saclay - CentraleSupélec ( email )

Gif-sur-Yvette, 91190
France

Ban Zheng (Contact Author)

Ecole Polytechnique ( email )

Route de Saclay
Palaiseau, 91 91128
France

HSBC Global Asset Management ( email )

110, esplanade du Général de Gaulle
Paris La Défense, 92400
France

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