Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multi-Factor Framework

Big Data and Machine Learning in Quantitative Investment, Wiley finance series. 2018

Posted: 16 Aug 2018

See all articles by Tony Guida

Tony Guida

Université de Savoie - Finance and Banking; RAM Active Investments

Guillaume Coqueret

EMLYON Business School

Date Written: February 28, 2018

Abstract

In this chapter, we apply a popular Machine Learning approach (extreme gradient boosted trees) to build enhanced diversified equity portfolios. A simple naïve equally-weighted portfolio of US stocks based on a boosted tree-based signal generates on average an excess return of 3.1% per annum, compared to a simple multifactor portfolio. We demonstrate that using boosted trees on a large number of features give an average error rate of 20% for predicting the 12-month sector neutral outperformance of a stock. In addition, enhancing a simple multi-factor signal with an ML-boosted signal proves to add value on a risk-return basis without altering the factor exposure of the traditional multi-factor portfolio.

Keywords: Machine learning, Factor Investing, Stock selection, Portfolio construction, Quantitative investment

JEL Classification: G11, C61

Suggested Citation

Guida, Tony and Coqueret, Guillaume, Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multi-Factor Framework (February 28, 2018). Big Data and Machine Learning in Quantitative Investment, Wiley finance series. 2018, Available at SSRN: https://ssrn.com/abstract=3225288

Tony Guida (Contact Author)

Université de Savoie - Finance and Banking ( email )

27 Rue Marcoz
Chambéry, 73011
France

RAM Active Investments ( email )

8 rue du rhone
geneva, 1204
Switzerland

Guillaume Coqueret

EMLYON Business School ( email )

23 Avenue Guy de Collongue
Ecully, 69132
France

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