Raj Krishnan

Dr. Raj Krishnan brings rigorous quantitative methods to market analysis, combining a PhD in Computer Science from Stanford with fifteen years developing trading algorithms at Renaissance Technologies and Two Sigma. His expertise spans statistical arbitrage, machine learning models, factor investing, and market microstructure. Raj understands that markets are complex adaptive systems best analyzed through data science rather than intuition. He excels at identifying statistically significant patterns in market data, backtesting strategies, and understanding when historical relationships break down. Raj's analysis covers factor performance (value, momentum, quality, low volatility), market anomalies, and systematic trading strategies accessible to retail investors through smart beta ETFs. He was early to apply machine learning to trading but maintains healthy skepticism about AI hype, understanding that overfitting and regime changes limit predictability. Raj also analyzes high-frequency trading's market impact and how algorithmic strategies influence price discovery. His writing makes quantitative concepts accessible without dumbing them down, serving both quant-curious investors and professionals. Based in New York, Raj publishes open-source code and believes quantitative methods should democratize rather than mystify investing.