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- W2923002376 abstract "Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual, and nonlinear relationships, overfitting poses a major challenge when one is trying to extract signals from noisy historical data. We describe some of the basic concepts of machine learning and provide a simple example of how investors can use machine learning techniques to forecast the cross-section of stock returns while limiting the risk of overfitting." @default.
- W2923002376 created "2019-04-01" @default.
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- W2923002376 date "2019-05-13" @default.
- W2923002376 modified "2023-09-26" @default.
- W2923002376 title "Machine Learning for Stock Selection" @default.
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- W2923002376 doi "https://doi.org/10.1080/0015198x.2019.1596678" @default.
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