This paper proposes a machine learning approach to building investment strategies that addresses several drawbacks of a classic approach. To demonstrate our approach, we use a logistic regression algorithm to build a time-series dual momentum trading strategy on the S&P 500 Index. Our algorithm outperforms both buy-and-hold and several base-case dual momentum strategies, significantly increasing returns and reducing risk. Applying the algorithm to other U.S. and international large capitalization equity indices generally yields improvements in risk-adjusted performance.

Keywords: Trading Strategy, Active Investing, Dual Momentum, Machine Learning, Logistic Regression, Trend-Following

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