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Gatta F, Iorio C, Chiaro D, Giampaolo F, Cuomo S. Statistical arbitrage in the stock markets by the means of multiple time horizons clustering. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08313-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
AbstractNowadays, 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.
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Abry P, Boniece BC, Didier G, Wendt H. Wavelet eigenvalue regression in high dimensions. STATISTICAL INFERENCE FOR STOCHASTIC PROCESSES 2022. [DOI: 10.1007/s11203-022-09279-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Ciuperca G. Real-time detection of a change-point in a linear expectile model. Stat Pap (Berl) 2022. [DOI: 10.1007/s00362-021-01278-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Steland A. Testing and estimating change-points in the covariance matrix of a high-dimensional time series. J MULTIVARIATE ANAL 2020. [DOI: 10.1016/j.jmva.2019.104582] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Horváth L, Rice G. Asymptotics for empirical eigenvalue processes in high-dimensional linear factor models. J MULTIVARIATE ANAL 2019. [DOI: 10.1016/j.jmva.2018.07.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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