Statistical arbitrage in the stock markets by the means of multiple time horizons clustering

Nowadays, statistical arbitrage is one of the most attractive fields of study for researchers, and its applications are widely used also in the financial industry. In this work, we propose a new approach for statistical arbitrage based on clustering stocks according to their exposition on common risk factors. A linear multifactor model is exploited as theoretical background. The risk factors of such a model are extracted via Principal Component Analysis by looking at different time granularity. Furthermore, they are standardized to be handled by a feature selection technique, namely the Adaptive Lasso, whose aim is to find the factors that strongly drive each stock's return. The assets are then clustered by using the information provided by the feature selection, and their exposition on each factor is deleted to obtain the statistical arbitrage. Finally, the Sequential Least SQuares Programming is used to determine the optimal weights to construct the portfolio. The proposed methodology is tested on the Italian, German, American, Japanese, Brazilian, and Indian Stock Markets. Its performances, evaluated through a Cross-Validation approach, are compared with three benchmarks to assess the robustness of our strategy.

Automated summary

This paper proposes a new statistical arbitrage approach by clustering stocks based on their exposure to common risk factors. A linear multifactor model is used as the theoretical background, and risk factors are extracted through Principal Component Analysis. The Adaptive Lasso technique is employed to standardize and select these factors, and assets are then clustered and their exposure to each factor is removed to achieve statistical arbitrage. Optimal weights for constructing the portfolio are determined using Sequential Least SQuares Programming. The methodology is tested on multiple stock markets and evaluated for robustness against three benchmarks using Cross-Validation.