How does statistical arbitrage scale pairs trading to hundreds of positions?

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AcadiFi · Accepted Answer

Statistical arbitrage (stat arb) scales mean-reversion trading across large universes using factor models and portfolio optimization. Consider Blackdrift Capital running $1.5B across 1100 US equities. Mechanics. Step one: factor decomposition. For each stock, decompose returns into systematic components (market, size, value, momentum, sector) via a risk model. The residual return after factor neutralization is idiosyncratic. Step two: signal construction. Compute each stock's residual return over 5-10 days and rank. Strong negative residual = oversold (buy candidate). Strong positive residual = overbought (sell candidate). Additional signals layered in: earnings surprise drift, liquidity provision, short interest reversals. Combined z-score per stock. Step three: portfolio construction. Optimizer maximizes expected return subject to constraints: dollar neutral (long = short), beta 0, sector neutral (±1%), factor neutral, max position 50 bps, gross leverage 4-6x, turnover cap 400-600% annually. Step four: execution. 1100 positions rebalanced daily or intraday. Execution via smart order routers with TCA (transaction cost analysis); avg slippage 5-10 bps. Distinguishes from pairs trading: pairs trades 30-60 named pairs with manual judgment; stat arb trades 800-1500 automatically with factor-model-driven signals and optimizer-driven sizing. Returns: 8-14% net, vol 4-7%, Sharpe 1.5-2.5 for top-tier funds, but the strategy has crowded-trade risk. August 2007 'quant quake' saw stat arb drawdowns of 10-15% in days when many funds deleveraged simultaneously. Monitor crowding via factor returns, borrow utilization, and fund flow data. Higher-frequency stat arb (sub-daily) approaches trading costs limits and needs colocation.

How does statistical arbitrage scale pairs trading to hundreds of positions?

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