On statistical arbitrage under a conditional factor model of equity returns

ArXiv ID: 2309.02205 “View on arXiv”

Authors: Unknown

Abstract

We consider a conditional factor model for a multivariate portfolio of United States equities in the context of analysing a statistical arbitrage trading strategy. A state space framework underlies the factor model whereby asset returns are assumed to be a noisy observation of a linear combination of factor values and latent factor risk premia. Filter and state prediction estimates for the risk premia are retrieved in an online way. Such estimates induce filtered asset returns that can be compared to measurement observations, with large deviations representing candidate mean reversion trades. Further, in that the risk premia are modelled as time-varying quantities, non-stationarity in returns is de facto captured. We study an empirical trading strategy respectful of transaction costs, and demonstrate performance over a long history of 29 years, for both a linear and a non-linear state space model. Our results show that the model is competitive relative to the results of other methods, including simple benchmarks and other cutting-edge approaches as published in the literature. Also of note, while strategy performance degradation is noticed through time – especially for the most recent years – the strategy continues to offer compelling economics, and has scope for further advancement.

Keywords: State space model, Factor model, Statistical arbitrage, Kalman filter, Latent risk premia, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical techniques like state-space frameworks, Kalman filters, and unscented variants, indicating high complexity. It also presents a detailed empirical study with 29 years of backtesting data, transaction cost considerations, and benchmark comparisons against other methods, showing substantial empirical rigor.

  flowchart TD
    A["Research Goal: Evaluate statistical arbitrage using a conditional factor model with latent, time-varying risk premia"] --> B["Input: 29-year history of US Equities data"]
    B --> C["Methodology: State Space Model (Linear & Non-linear)"]
    C --> D["Computational Process: Online Kalman Filter & State Prediction"]
    D --> E["Output: Filtered returns & Residuals (Deviations from Latent Risk Premia)"]
    E --> F["Trading Strategy: Mean Reversion on Residuals (Transaction Cost Adjusted)"]
    F --> G["Key Findings: Competitive returns over 29 years despite recent degradation; Outperforms benchmarks"]