A Multi-factor Adaptive Statistical Arbitrage Model

Wenbin Zhang, Zhen Dai, Bindu Pan and Milan Djabirov

Papers from arXiv.org

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.

Date: 2014-05
New Economics Papers: this item is included in nep-cmp and nep-fmk
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1405.2384

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