Building portfolios based on machine learning predictions. Kaczmarek, T. & Perez, K. Economic Research-Ekonomska Istraživanja, 35(1):19–37, December, 2022. 8 citations (Semantic Scholar/DOI) [2023-06-19]![Building portfolios based on machine learning predictions [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{kaczmarek_building_2022,
title = {Building portfolios based on machine learning predictions},
volume = {35},
issn = {1331-677X, 1848-9664},
url = {https://www.tandfonline.com/doi/full/10.1080/1331677X.2021.1875865},
doi = {10.1080/1331677X.2021.1875865},
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.},
language = {en},
number = {1},
urldate = {2023-06-17},
journal = {Economic Research-Ekonomska Istraživanja},
author = {Kaczmarek, Tomasz and Perez, Katarzyna},
month = dec,
year = {2022},
note = {8 citations (Semantic Scholar/DOI) [2023-06-19]},
keywords = {/unread},
pages = {19--37},
}
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