Machine Learning Portfolios with Equal Risk Contributions

30 Pages Posted: 8 Aug 2019 Last revised: 1 Sep 2019

See all articles by Alexandre Rubesam

Alexandre Rubesam

IESEG School of Management; French National Center for Scientific Research (CNRS) - Lille Economie & Management (LEM) UMR 9221

Date Written: August 6, 2019

Abstract

We use machine learning methods to forecast individual stock returns and create long-short portfolios in the Brazilian stock market, using a rich data set including technical and fundamental predictive signals. We further develop an algorithm that combines long-short portfolios obtained with various machine learning methods such that (i) the risk contributions of all individual long-short portfolios are equal, and (ii) the aggregate risk contribution of all long positions equals that of all short positions. Compared with an equally-weighted combination of the original portfolios, the portfolio obtained with our algorithm has over twice the Sharpe ratio and less than a third of the maximum drawdown.

Keywords: machine learning, forecasting, return prediction, risk parity, equal risk contribution

JEL Classification: C53, G11, G15

Suggested Citation

Rubesam, Alexandre, Machine Learning Portfolios with Equal Risk Contributions (August 6, 2019). Available at SSRN: https://ssrn.com/abstract=3432760 or http://dx.doi.org/10.2139/ssrn.3432760

Alexandre Rubesam (Contact Author)

IESEG School of Management ( email )

Socle de la Grande Arche
1 Parvis de la Defense
Puteaux, Paris 92800
France

French National Center for Scientific Research (CNRS) - Lille Economie & Management (LEM) UMR 9221 ( email )

Lille
France

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