Boosting Portfolio Choice in the Big Data Era

48 Pages Posted: 17 Dec 2019 Last revised: 4 Sep 2020

See all articles by Hongwei Zhang

Hongwei Zhang

Tilburg University - TIAS School for Business and Society; Central University of Finance and Economics (CUFE)

Ben Jacobsen

Tilburg University - TIAS School for Business and Society; Massey University

Fuwei Jiang

Central University of Finance and Economics (CUFE)

Date Written: November 28, 2019

Abstract

Markowitz’s mean-variance portfolio optimization is either inefficient or impossible when the number of assets becomes relatively large. To overcome this difficulty, we propose several component-wise boosting learning methods that, in a linear regression specification, can iteratively select the assets (variables) with the largest contribution to the fit from a huge number of assets, and finally form the tangency portfolio. Based on dataset consisting of 897 assets with 624 observations from Ken French data library, we assess the performance of tangency portfolios estimated using our methods. We find that our methods substantially outperform the 1/N portfolio in terms of various popular metrics. For example, our component-wise LogitBoost can reach an out-of-sample Sharpe ratio of 1.03, while the 1/N portfolio achieves a Sharpe ratio of only 0.27.

Keywords: Portfolio Selection, Machine Learning, Boosting

JEL Classification: G11, G12, C52, C55

Suggested Citation

Zhang, Hongwei and Jacobsen, Ben and Jiang, Fuwei, Boosting Portfolio Choice in the Big Data Era (November 28, 2019). AFA 2021 Annual Meeting, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3495901 or http://dx.doi.org/10.2139/ssrn.3495901

Hongwei Zhang (Contact Author)

Tilburg University - TIAS School for Business and Society ( email )

Warandelaan 2
TIAS Building
Tilburg, Noord Brabant 5037 AB
Netherlands

Central University of Finance and Economics (CUFE)

39 South College Road
Haidian District
Beijing, 100081
China

Ben Jacobsen

Tilburg University - TIAS School for Business and Society ( email )

Warandelaan 2
TIAS Building
Tilburg, Noord Brabant 5037 AB
Netherlands

Massey University ( email )

Auckland
New Zealand

Fuwei Jiang

Central University of Finance and Economics (CUFE) ( email )

39 South College Road
Haidian District
Beijing, Beijing 100081
China

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