Quantitative Finance

Quantitative finance applies mathematical and computational methods to asset pricing, forecasting, portfolio allocation, risk measurement, derivatives, and market microstructure. Common approaches include factor models, econometric time-series methods, deep learning, graph models, and reinforcement learning, alongside tools such as stochastic volatility models and no-arbitrage constraints. Newer work also uses generative models and LLM agents to discover trading signals, simulate markets, and automate research, with evaluation spanning equity panels, options, order books, and portfolio backtests. A central challenge is turning predictive gains into reliable returns after transaction costs and across changing regimes, while preventing leakage and backtest overfitting; simpler econometric baselines often remain difficult to beat.