A Multi-factor Adaptive Statistical Arbitrage Model

TL;DR

This paper proposes a multi-factor adaptive statistical arbitrage model that uses clustering and graphical lasso for portfolio selection, demonstrating improved performance and statistical arbitrage validity over traditional methods.

Contribution

It introduces a hybrid multi-factor approach combining clustering and graphical lasso, and evaluates adaptive re-computation, with findings on their relative effectiveness.

Findings

Clustering outperforms graphical lasso in candidate portfolio selection.

Hybrid approach of clustering and graphical lasso yields the best results.

Adaptive re-computation does not significantly improve trading outcomes.

Abstract

This paper examines the implementation of a statistical arbitrage trading strategy based on co-integration relationships where we discover candidate portfolios using multiple factors rather than just price data. The portfolio selection methodologies include K-means clustering, graphical lasso and a combination of the two. Our results show that clustering appears to yield better candidate portfolios on average than naively using graphical lasso over the entire equity pool. A hybrid approach of using the combination of graphical lasso and clustering yields better results still. We also examine the effects of an adaptive approach during the trading period, by re-computing potential portfolios once to account for change in relationships with passage of time. However, the adaptive approach does not produce better results than the one without re-learning. Our results managed to pass the test for the presence of statistical arbitrage test at a statistically significant level. Additionally we were able to validate our findings over a separate dataset for formation and trading periods.