Paper: arXiv 2406.06706

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

Cited References (14)

  1. Testing market efficiency using statistical arbitrage with applications to momentum and value strategies (2004)
  2. Statistical arbitrage in the US equities market (2010)
  3. Pairs trading and selection methods: is cointegration superior? (2015)
  4. Pairs Trading: Quantitative Methods and Analysis (2011)
  5. Portfolio Optimization with Robust Covariance and Conditional Value-at-Risk Constraints — → 2406.00610
  6. Cointegration and error correction: Representation, estimation, and testing (1987)
  7. Asset Allocation: Combining Investor Views with Market Equilibrium (1990)
  8. Portfolio selection (1952)
and 6 more
  1. A step-by-step guide to the Black-Litterman model (2007)
  2. Indexing and statistical arbitrage (2005)
  3. The high sensitivity of pairs trading returns (2013)
  4. Real-time segmentation of on-line handwritten arabic script (2014)
  5. Fast classification of handwritten on-line Arabic characters (2014) — link
  6. Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications (2018) — link

1 of 14 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.