Performance of Pairs Trading Strategies Based on Principal Component Analysis Methods

Yufei Sun ()
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Yufei Sun: Faculty of Economic Sciences, University of Warsaw

No 2025-21, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: This thesis examines market-neutral, mean-reversion-based statistical arbitrage strategies in the Chinese equity market, using two factor decomposition methods: Principal Component Analysis (PCA) and sector-based Exchange-Traded Funds (ETFs). Residual returns are modeled as mean-reverting Ornstein–Uhlenbeck (OU) processes, generating contrarian signals. A 60-day rolling window ensures out-of-sample estimation. Realistic frictions are included via a 10-basis-point round-trip cost. Backtests from 2005 to 2024 compare four configurations: synthetic ETFs, fixed PCA, dynamic PCA, and trading-time volume adjustments. Both PCA- and ETF-based strategies deliver robust Sharpe ratios near 0.90–0.95. PCA portfolios perform better under high cross-sectional volatility, while ETF-based models remain stable during structural shifts. Incorporating trading volume enhances returns, especially for ETF models. Sensitivity analysis highlights the importance of threshold tuning and rolling-window lengths. These findings stress the critical role of factor construction and signal design in market-neutral strategies, suggesting further improvement via adaptive PCA and volume-weighted signals.

Keywords: Quantitative Trading; Pair Trading; Principal Component Analysis; Chinese Stock Market (search for similar items in EconPapers)
JEL-codes: C32 C58 G11 G12 G17 (search for similar items in EconPapers)
Pages: 76 pages
Date: 2025
New Economics Papers: this item is included in nep-ets
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