Applying Machine Learning to Trading Strategies: Using Logistic Regression to Build Momentum-Based Trading Strategies

78 Pages Posted: 4 Feb 2019 Last revised: 14 Mar 2019

See all articles by Patrick Beaudan

Patrick Beaudan

Northern Trust Corporation; Emotomy

Shuoyuan He

Tulane University - A.B. Freeman School of Business

Date Written: January 30, 2019

Abstract

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

JEL Classification: C10, G12, G14, G17

Suggested Citation

Beaudan, Patrick and He, Shuoyuan, Applying Machine Learning to Trading Strategies: Using Logistic Regression to Build Momentum-Based Trading Strategies (January 30, 2019). Available at SSRN: https://ssrn.com/abstract=3325656 or http://dx.doi.org/10.2139/ssrn.3325656

Patrick Beaudan (Contact Author)

Northern Trust Corporation ( email )

50 South LaSalle Street
Chicago, IL 60603
United States
415 839 5239 (Phone)

Emotomy ( email )

580 California Street
San Francisco, CA 94104
United States

HOME PAGE: http://www.emotomy.com

Shuoyuan He

Tulane University - A.B. Freeman School of Business ( email )

7 McAlister Drive
New Orleans, LA 70118
United States

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