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 it utilizes a wide variety of advanced knowledge and techniques from probability, stochastic processes, functional analysis, numerical analysis, Monte Carlo simulations, stochastic control and partial differential equations. My recently completed and ongoing research projects include the 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 designed to utilize historical data to improve the prediction accuracy of credit rating. Specifically, we developed new metrics that can be applied to any artificial intelligence methods and created a new ensemble structure of artificial neural networks. We then used real-world data to validate our proposed model for credit rating prediction through performance measures. In another project, we applied sequence-based clustering to group firms with similar transition behavior and credit risk assessment was performed using representative transition matrices of the clusters. The proposed clustering model was evaluated under three long-term classification scenarios.
