Dynamic Cross-sectional Regime Identification for Financial Market Prediction

We investigate issues related to dynamic cross-sectional regime identification for financial market prediction. A financial market can be viewed as an ecosystem regulated by regimes that may switch at different time points. In most existing regime-based prediction models, regimes can only switch, according to a static transition probability matrix, among a fixed set of regimes identified on training data due to the fact that they lack in mechanism of identifying new regimes on test data. This prevents them from being effective as the financial markets are time-evolving and may fall into a new regime at any future time. Moreover, most of them only handle single time series, and are not capable of dealing with multiple time series. These shortcomings prompted us to devise a dynamic cross-sectional regime identification model for time series prediction. The new model is defined on a multi-time-series system, with time-varying transition probabilities, and can identify new cross-sectional regimes dynamically from the time-evolving financial market. Experimental results on real-world financial datasets illustrate the promising performance and suitability of our model.