Research Interests

Mathematical Finance and Stochastic Analysis;

My primary research interest includes stochastic modeling, risk measurement and management, portfolio optimization, analysis of financial market, quantitative finance, data analytics, and Artificial intelligence. This is an interesting research area with fascinating and challenging research problems, and uses a wide variety of advanced knowledge and techniques from probability, stochastic processes, functional analysis, numerical analysis, Monte Carlo, stochastic control and partial differential equations. My recently completed and ongoing research projects include Development of derivatives valuation models, Optimal investment strategies, Portfolio management, Risk assessment, and Machine learning.

In the recent project, we proposed a novel learning algorithm that has been designed to utilize historical data to improve the prediction accuracy of credit rating.  Specifically, we built new metrics that can be applied to any artificial intelligence methods and developed a new ensemble structure of artificial neural networks, and then use real-world data to validate our proposed model for credit rating prediction via performance measures. In the other project, we applied sequence-based clustering for grouping firms with similar transition behavior and credit risk assessment is made by representative transition matrices of the clusters. The proposed clustering model is evaluated under three long-term classification scenarios.