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Financial crisis detection is a long-standing challenging issue with significant practical values and impact on economy, society and globalization. The challenge lies in many aspects, in particular, the nonlinear and dynamic characteristics associated with financial crisis. Most of existing methods rely on selecting individual indicators associated with one market indicator, and the linear assumption is often behind the models for prediction. In practice, a linear assumption may be too strong to be applicable to the real market dynamics. More importantly, instruments in different markets such as gold price and petrol price are often coupled. A financial crisis may significantly change the couplings between different market indicators. In addition, such couplings in cross-market interaction are likely nonlinear. In this paper, we present a new approach for financial crisis detection by catering for the often nonlinear couplings between major indicators selected from different markets, called coupled market behavior analysis, to detect different coupled market behaviors at crisis and non-crisis periods. A Coupled Hidden Markov Model (CHMM) is built to characterize the coupled market behaviors of equity, commodity and interest markets as case studies. The empirical results show the need of catering for nonlinear couplings between various markets and the proposed approach is much more effective in capturing the coupling and nonlinear relations associated with financial crisis compared with other traditionally used approaches, such as Signal, Logistic and ANN models.

Coupled behaviors, which refer to behaviors having some relationships between them, are usually seen in many real-world scenarios, especially in stock markets. Recently, the coupled hidden Markov model (CHMM)-based coupled behavior analysis has been proposed to consider the coupled relationships in a hidden state space. However, it requires aggregation of the behavioral data to cater for the CHMM modeling, which may overlook the couplings within the aggregated behaviors to some extent. In addition, the Markov assumption limits its capability to capturing temporal couplings. Thus, this paper proposes a novel graph-based framework for detecting abnormal coupled behaviors. The proposed framework represents the coupled behaviors in a graph view without aggregating the behavioral data and is flexible to capture richer coupling information of the behaviors (not necessarily temporal relations). On top of that, the couplings are learned via relational learning methods and an efficient anomaly detection algorithm is proposed as well. Experimental results on a real-world data set in stock markets show that the proposed framework outperforms the CHMMbased one in both technical and business measures.U.S. Government work not protected by U.S. copyright WCCI 2012 IEEE World Congress on Computational Intelligence June, 10-15, 2012 -Brisbane, Australia IJCNN