The global financial crisis occurred in 2008 and its contagion to other regions, as well as the long-lasting impact on different markets, show that it is increasingly important to understand the complicated coupling relationships across financial markets. This is indeed very difficult as complex hidden coupling relationships exist between different financial markets in various countries, which are very hard to model. The couplings involve interactions between homogeneous markets from various countries (we call intra-market coupling), interactions between heterogeneous markets (inter-market coupling) and interactions between current and past market behaviors (temporal coupling). Very limited work has been done towards modeling such complex couplings, whereas some existing methods predict market movement by simply aggregating indicators from various markets but ignoring the inbuilt couplings. As a result, these methods are highly sensitive to observations, and may often fail when financial indicators change slightly. In this paper, a coupled deep belief network is designed to accommodate the above three types of couplings across financial markets. With a deep-architecture model to capture the high-level coupled features, the proposed approach can infer market trends. Experimental results on data of stock and currency markets from three countries show that our approach outperforms other baselines, from both technical and business perspectives.

The global financial crisis occurred in 2007 and its severe damaging consequences on other global financial markets, show the great importance of understanding the impact and contagion between different financial markets. A variety of methods have been proposed and implemented on market contagion. However, most of the existing literature simply test the existence of market contagion in financial crisis, and there is limited work go deep to investigate the complex market couplings which are the essence of market contagion. This is indeed very difficult as it involves the selection of discriminative indicators, the different types of couplings (intra-market coupling, inter-market coupling), the hidden characteristic of couplings, and the evaluation of market couplings in understanding crisis. To address these issues, this paper proposes a CHMM-LR framework to investigate the relations between financial crisis and three pairwise market couplings from three typical global financial markets: Equity market, Commodity market and Interest market. We adopt Coupled Hidden Markov Model (CHMM) to capture the complex hidden pairwise market couplings, and the financial crisis forecasting abilities based on different pairwise market couplings are imported to measure the relations by Logistic Regression (LR). Experiments of real financial data during the period 1990 to 2010 show the advantages of market couplings in understanding crisis. In addition, the experimental results provide crucial interpretation for the 2008 global financial crisis periods identification.

Nowadays, most recommender systems (RSs) mainly aim to suggest appropriate items for individuals. Due to the social nature of human beings, group activities have become an integral part of our daily life, thus motivating the study on group RS (GRS). However, most existing methods used by GRS make recommendations through aggregating individual ratings or individual predictive results rather than considering the collective features that govern user choices made within a group. As a result, such methods are heavily sensitive to data, hence they often fail to learn group preferences when the data are slightly inconsistent with predefined aggregation assumptions. To this end, we devise a novel GRS approach which accommodates both individual choices and group decisions in a joint model. More specifically, we propose a deep-architecture model built with collective deep belief networks and dual-wing restricted Boltzmann machines. With such a deep model, we can use high-level features, which are induced from lower-level features, to represent group preference so as to relieve the vulnerability of data. Finally, the experiments conducted on a real-world dataset prove the superiority of our deep model over other state-of-the-art methods.

Choice modeling (CM) aims to describe and predict choices according to attributes of subjects and options. If we presume each choice making as the formation of link between subjects and options, immediately CM can be bridged to link analysis and prediction (LAP) problem. However, such a mapping is often not trivial and straightforward. In LAP problems, the only available observations are links among objects but their attributes are often inaccessible. Therefore, we extend CM into a latent feature space to avoid the need of explicit attributes. Moreover, LAP is usually based on binary linkage assumption that models observed links as positive instances and unobserved links as negative instances. Instead, we use a weaker assumption that treats unobserved links as pseudo negative instances. Furthermore, most subjects or options may be quite heterogeneous due to the long-tail distribution, which is failed to capture by conventional LAP approaches. To address above challenges, we propose a Bayesian heteroskedastic choice model to represent the non-identically distributed linkages in the LAP problems. Finally, the empirical evaluation on real-world datasets proves the superiority of our approach.

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.