Factor investing targets specific drivers of return that have been identified through decades of academic research. The most established factors include value (cheap stocks outperform), momentum (recent winners continue winning), quality (profitable companies outperform), size (small stocks outperform), and low volatility (less volatile stocks deliver higher risk-adjusted returns).
Multi-factor models combine several factors to build diversified portfolios that are less dependent on any single return driver. Professional quant funds continuously research new factors and optimize their combinations.
Statistical arbitrage (stat arb) uses mathematical models to identify mispricings between related securities. The classic example is pairs trading: finding two historically correlated stocks that have temporarily diverged, going long the underperformer and short the outperformer, and waiting for convergence.
Modern stat arb strategies operate across thousands of securities simultaneously, use complex cointegration and machine learning models, and require sophisticated risk management. These strategies typically generate many small gains, relying on the law of large numbers for consistent profitability.
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