Quantitative Finance Research Papers

Volume-synchronized probability of informed trading as a practical signal for detecting adverse selection in fragmented equity markets. Includes Python implementation, volume bucketing, and trade classification methods.

Cluster-based portfolio allocation that avoids covariance inversion and delivers more stable out-of-sample diversification. Includes correlation distance, recursive bisection, and full Python implementation.

A statistically rigorous alternative to raw Sharpe that adjusts for non-normality, sample length, skewness, and kurtosis. Detects backtest overfitting with inferential statistics.

Quoted, effective, and realized spreads as microstructure state variables. Decomposing the cost of immediacy for execution models with adverse selection analysis.

Why PnL arises from signal after implementation, not signal alone. Fill models, latency decomposition, square-root market impact, and Python fill simulators.