Abstract
Regime switching analysis is extensively advocated in many fields to capture complex behaviors underlying an ecosystem, such as the economic or financial system. A regime can be defined as a specific group of complex patterns that share common characteristics in a specific time interval. Regime switch, caused by external and/or internal drivers, refers to the changing behaviors exhibited by a system at a specific time point. The existing regime detection methods suffer from two drawbacks: they lack the capability to identify new regimes dynamically or they ignore the cross-sectional dependencies exhibited by time series data for the forecasting. This promoted us to devise a cluster-based regime identification model that can identify cross-sectional regimes dynamically with a time-varying transition probability, and capture cross-sectional dependencies underlying financial time series for market forecasting. Our approach makes use of a nonlinear model to account for the cross-sectional regime dependencies, neglected by most existing studies, that can improve the performance of a forecasting model significantly. Experimental results on both synthetic and real-world dataset demonstrate that our model outperforms state-of-the-art forecasting models.
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Acknowledgements
This work was supported by NSERC CRD - Quebec Prompt - Laplace Insights - EAM - joint program under the grants CRDPJ 537461-18 and 114-IA-Wang-DRC 2019 to S. Wang and Chinese Scholarship Council scholarship to R. Chen.
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Chen, R., Sun, M., Xu, K., Patenaude, JM., Wang, S. (2022). Clustering-Based Cross-Sectional Regime Identification for Financial Market Forecasting. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13427. Springer, Cham. https://doi.org/10.1007/978-3-031-12426-6_1
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