This project advances machine learning methods for statistical arbitrage and portfolio optimization by focusing on high-frequency financial time-series and optimal control problems in low-latency environments. It develops techniques for high-dimensional feature extraction, data-driven models that capture complex temporal dependencies, and optimization frameworks that convert predictive signals into effective execution strategies and portfolio decisions.
