Original scientific paper
https://doi.org/10.1080/1331677X.2021.1875865
Building portfolios based on machine learning predictions
Tomasz Kaczmarek
Katarzyna Perez
Abstract
This paper demonstrates that portfolio optimization techniques
represented by Markowitz mean-variance and Hierarchical Risk
Parity (HRP) optimizers increase the risk-adjusted return of portfolios built with stocks preselected with a machine learning tool.
We apply the random forest method to predict the cross-section
of expected excess returns and choose n stocks with the highest
monthly predictions. We compare three different techniques—
mean-variance, HRP, and 1/N— for portfolio weight creation using
returns of stocks from the S&P500 and STOXX600 for robustness.
The out-of-sample results show that both mean-variance and HRP
optimizers outperform the 1/N rule. This conclusion is in the
opposition to a common criticism of optimizers’ efficiency and
presents a new light on their potential practical usage.
Keywords
Portfolio optimization; artificial intelligence in finance; random forest; equities; forecasts
Hrčak ID:
301672
URI
Publication date:
31.3.2023.
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