Causal and Predictive Modeling of Short-Horizon Market Risk and Systematic Alpha Generation Using Hybrid Machine Learning Ensembles

TL;DR

This paper introduces a hybrid machine learning ensemble framework for short-term market risk prediction and alpha generation, demonstrating significant risk-adjusted returns and interpretability over a 20-year period.

Contribution

It develops a novel hybrid ensemble model combining neural networks and tree-based methods for forecasting short-horizon market risk and identifying key drivers, advancing systematic trading strategies.

Findings

Sharpe ratio of 2.51 achieved

Annualized CAPM alpha of +0.28

Model captures nonlinear risk dynamics effectively

Abstract

We present a systematic trading framework that forecasts short-horizon market risk, identifies its underlying drivers, and generates alpha using a hybrid machine learning ensemble built to trade on the resulting signal. The framework integrates neural networks with tree-based voting models to predict five-day drawdowns in the S&P 500 ETF, leveraging a cross-asset feature set spanning equities, fixed income, foreign exchange, commodities, and volatility markets. Interpretable feature attribution methods reveal the key macroeconomic and microstructural factors that differentiate high-risk (crash) from benign (non-crash) weekly regimes. Empirical results show a Sharpe ratio of 2.51 and an annualized CAPM alpha of +0.28, with a market beta of 0.51, indicating that the model delivers substantial systematic alpha with limited directional exposure during the 2005--2025 backtest period. Overall, the findings underscore the effectiveness of hybrid ensemble architectures in capturing nonlinear risk dynamics and identifying interpretable, potentially causal drivers, providing a robust blueprint for machine learning-driven alpha generation in systematic trading.