Application of Black-Litterman Bayesian in Statistical Arbitrage

ArXiv ID: 2406.06706 “View on arXiv”

Authors: Unknown

Abstract

\begin{“abstract”} In this paper, we integrated the statistical arbitrage strategy, pairs trading, into the Black-Litterman model and constructed efficient mean-variance portfolios. Typically, pairs trading underperforms under volatile or distressed market condition because the selected asset pairs fail to revert to equilibrium within the investment horizon. By enhancing this strategy with the Black-Litterman portfolio optimization, we achieved superior performance compared to the S&P 500 market index under both normal and extreme market conditions. Furthermore, this research presents an innovative idea of incorporating traditional pairs trading strategies into the portfolio optimization framework in a scalable and systematic manner.

Keywords: Statistical Arbitrage, Pairs Trading, Black-Litterman Model, Mean-Variance Portfolio, Asset Allocation, Equities

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper integrates advanced Bayesian methods (Black-Litterman) with co-integration analysis, involving rigorous statistical formulas, but is heavily backtest-driven with specific date ranges, prescreening logic, and reported out-of-sample performance, indicating strong implementation focus.

  flowchart TD
    A["Research Goal<br>Develop scalable arbitrage strategy<br>using Black-Litterman & Pairs Trading"] --> B["Data Collection<br>Historical Equity Data<br>Market Regimes (Normal/Extreme)"]
    B --> C["Statistical Arbitrage<br>Pair Selection &<br>Spread Deviation Analysis"]
    C --> D["Black-Litterman Input<br>Implied Equilibrium Returns<br>Trusted Investor Views"]
    D --> E["Bayesian Integration<br>Posterior Expected Returns<br>Portfolio Optimization"]
    E --> F["Outcome<br>Efficient Mean-Variance Portfolio<br>Outperforms S&P 500<br>Robust to Market Conditions"]