Statistical Arbitrage

Statistical arbitrage trades relationships rather than direction. When two or more instruments that usually move together drift apart, a stat arb strategy sells the expensive side, buys the cheap side, and waits for the gap to close. The method is old, crowded and still widely used, and the difference between a strategy that works and one that does not usually lies in the details this course covers.

How does a statistical arbitrage strategy make money?

It takes many small, market-neutral bets that a spread will revert towards its usual level. Each bet has a modest edge. The return comes from running many of them at once, so that the noise in individual spreads averages out while the edge adds up. Market and sector exposure are hedged away, so the result should depend little on whether the market rises or falls.

Why do so many stat arb backtests fail live?

Three reasons recur. Costs, including borrowing fees for the short side, are larger than assumed. Relationships that held in the sample break when regimes change. And the universe used in the test often contains only the stocks that survived. The course builds a test that addresses all three before any result is trusted.

Who is this course for?

Quant traders and researchers who want a working method for relative-value strategies. It counts toward the Certificate in Statistical Arbitrage and the Professional Diploma in Algorithmic Trading.

What you will be able to do

• Explain what makes a spread mean-revert and how to test for it.
• Build residual signals from a factor model and trade them market-neutral.
• Design entry, exit and sizing rules for a portfolio of spreads.
• Estimate the effect of costs, borrowing and capacity on a stat arb strategy.
• Backtest a stat arb strategy without look-ahead or survivorship bias.

Syllabus, module by module

1. What statistical arbitrage is

   Relative value, market neutrality and the difference between statistical and pure arbitrage.

2. Mean reversion

   Stationarity, the Ornstein-Uhlenbeck process and estimating the speed of reversion.

3. From pairs to portfolios

   Distance, correlation and cointegration approaches, and why single pairs are fragile.

4. Residual signals

   Removing market, sector and factor exposure, and trading what is left.

5. Trading rules

   Entry and exit thresholds, holding periods, stops and position sizing across spreads.

6. Portfolio construction

   Neutralising beta, sector and factor exposure; turnover and concentration.

7. Costs and capacity

   Spread, impact, borrow fees and short availability, and how they cap the strategy.

8. Testing and decay

   Out-of-sample tests, crowding, regime change and why stat arb returns fall over time.

9. Project lab

   Supervised work on the assessed stat arb project.

How the certificate is earned

An assessed project: you build a market-neutral statistical arbitrage strategy on a universe of your choice, test it out of sample with costs, and submit the code with a report. On a pass you receive the Course certificate in Statistical Arbitrage, issued by the Institute with a verification code. Read how certification works.

Before you start

Working Python with pandas, regression, and the material of Time Series Analysis for Finance and Backtesting Methods.

• Time Series Analysis for Finance
• Backtesting: Methods, Pitfalls and Validation