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- W3126338114 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." @default.
- W3126338114 created "2021-02-15" @default.
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- W3126338114 date "2021-02-05" @default.
- W3126338114 modified "2023-10-11" @default.
- W3126338114 title "Building portfolios based on machine learning predictions" @default.
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- W3126338114 doi "https://doi.org/10.1080/1331677x.2021.1875865" @default.
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