Logistic Regression and Momentum-Based Trading Strategy
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Patrick Beaudan; Shuoyuan He
- National Trust
- ?Northern Trust (United States)
- ?Emotomy
- ?Northern Trust Corporation
- Tulane University
- ?Tulane University - A.B. Freeman School of Business
Strategy in a nutshell
The investment strategy focuses on the SPX Index, using normalized momentum (30–360 business days) and drawdowns (15–120 business days) to predict profitability. Features are scaled to -1 to +1 to generate a profitability vector indicating if future returns exceed 5% annually. Cubic polynomials capture non-linear patterns. Daily predictions use weighted combinations of features through a sigmoid function. Weights are optimized on historical data, and predictions are made three days ahead. If returns are predicted below the threshold, the strategy holds cash; otherwise, it invests in SPX at market close. The strategy is rebalanced daily for dynamic adaptation to market conditions.
Economic rationale
Momentum is a well-known factor, but machine learning enhances its application by adapting to changing market conditions and handling complex factor interactions. Polynomials allow non-linear relationships between features, enabling dynamic weighting based on market conditions. This method improves classical momentum strategies, including logistic regression-based models, and is flexible enough to apply to US or international equity indices, offering robustness and predictive power across diverse market environments.