Financial information processing and development of emerging financial markets

With the rapid development and globalization of financial markets (especially emerging financial markets), financial information processing has become a hot research area due to its immense practical applications. Such applications include stock market analysis, foreign exchange rate forecasting, option pricing, bank failure prediction, financial risk management, credit rating and scoring, bank loan management, customer relationship management, and antimoney laundering. Accordingly, there has been an increasing demand in using financial information processing techniques for many core financial tasks. Nevertheless, as a new cross-disciplinary field, the existing financial information processing methods are far from practical for scenarios in the global financial market; it is currently not clear how the information processing techniques, which are rapidly emerging, can be used to improve the quality of financial information processing. Therefore, it is necessary to conduct a thorough investigation of the financial information processing problems and understand its fundamental theoretical difficulties. At the same time, investigating the formulation mechanism and generic operations law for all kinds of financial markets is necessary for deep information processing. For this purpose, this special section presents new progress in financial information processing that is expected to trigger thought and deepen future research. In this section, a two-round peer review process was performed. In the first-round of the review process, 12 papers were selected from 16 invited talks and 46 submissions related to financial information processing from different countries and regions. After making necessary revisions in terms of reviewer’s recommendations, the second-round review process selected nine papers for final publication. The nine papers can be divided into three categories. The first category focuses on financial risk analysis and modeling. The first article applies a hidden Markov model to handle the default risk; it is titled “Modeling default risk via a hidden Markov model of multiple sequences” by Ching et al. The second paper emphasizes customer credit risk classification using support vector machine (SVM) ensemble learning technique, while the third paper proposes an interest force accumulated function model with Gauss process and Poisson process as the basis for the life insurance reserve model and meantime the risk caused by drawing reserve is analyzed in terms of a stochastic interest rates environment. They are “Developing an SVM-based ensemble learning system for customer risk identification collaborating with customer relationship management” by Yu et al., and “A class of life insurance reserve model and risk analysis in a stochastic interest rates environment” by Jia et al., respectively. In the second category, three papers are selected for covering strategic games, financial distress diagnosis and fund performance evaluation. These are named now in respective order. The fourth paper, titled “N-person