Research

A comprehensive analysis of three regime-based portfolio allocation strategies demonstrating that traditional 60/40 portfolios can outperform sophisticated machine learning approaches after accounting for transaction costs and implementation challenges.

A comparative analysis of CPU and GPU implementations for solving Hamilton-Jacobi-Bellman equations in high-frequency cryptocurrency market making, demonstrating up to 43% higher profitability in volatile conditions with sub-second latency.

A systematic approach to exploiting arbitrage opportunities in currency crashes through an early warning system based on domestic economic stress indicators, increasing crash probability prediction from 7.8% to 43% with 4-5 month lead time.

A comprehensive analysis of the Holiday Effect trading strategy exploiting pre-event price drift in Amazon stock around major shopping holidays, demonstrating a Sharpe ratio of 0.54 with 75.8% win rate across 33 trades from 1998-2025.

A systematic analysis of leveraged ETF volatility decay mechanics, demonstrating how daily rebalancing creates exploitable inefficiencies. The strategy achieves 12.3% CAGR with Sharpe ratio 1.45 by shorting 3x leveraged ETFs during high-volatility regimes.

A comprehensive analysis of an intraday momentum breakout strategy for E-mini futures employing volatility-based noise area boundaries and volatility-targeted position sizing. The strategy achieves 8.5% CAGR with Sharpe ratio 1.18 across 15 years (2011-2026) with maximum drawdown of 16.4%.

A novel pairs trading framework combining fractional cointegration, stochastic optimal control, and reinforcement learning. The strategy demonstrates 35% higher risk-adjusted returns versus benchmarks with Sharpe ratio 2.86-3.27 and maximum drawdown of only 5.4%.