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.
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Financial Analysts Journal
CFA Institute Member Content
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