Department of Systems Engineering & Engineering Management

Advances in computational stochastic programming and its applications in financial services

We develop high performance computational tools to solve large size multi-stage stochastic programming problems in real-time. Applications of stochastic programming models such as multiple stage portfolio selection, asset-liability management, derivative pricing, etc., are investigated.

Behavioral Portfolio Selection

We study portfolio selection models where the classical utility function is replaced by behavioral criteria featuring S-shaped value function and probability distortion. Implications on capital asset pricing will be investigated.

Dynamic Portfolio Selection with a Mean-Variance Formulation

The research goal is to seek optimal investment strategies for dynamic portfolio selection problems with a mean-variance formulation. To seek an optimal dynamic portfolio policy within a mean-variance framework implies to achieve a dual balance between the expected return and the risk and between the short term and long term benefits. Variance minimization is a notorious problem in stochastic control due to its associated property of nonseparability. Separation schemes can be developed to overcome this difficulty of nonseparability. Further research efforts are needed to improve the portfolio selection models and to derive full feedback optimal investment policies.

Electronic Commerce and Electronic Markets

J.X. Yu, C.C. Yang and W.Lam

Develop models and systems to support electronic commerce and electronic markets. We have developed the COALA system, which uses Sun's Java technologies, such as, Java RMI, and Bilateral Shapley Value (BSV), to simulate the power industry. We use such a system to study the impacts of deregulation of restructuring. Similar approaches have been applied to study other types of markets, for example, markets which are very dynamic or very sensitive to the public information.

Financial Digital Library

J. Yen, J. Yu, C. Yang and W. Lam

The Financial Digital Library being developed contains annual reports, financial news articles, and government documents that allows user from different places to access and search for the information they need based on concept space. We have a collection of annual reports from 249 Hong Kong public firms, real-time stock quotes, and a set of agents to support technical and fundamental analysis. We have also conducted a series of studies to study how an electronic filing system can improve transparency of financial information transmission in Hong Kong.

Index tracking with stochastic linear quadratic controls using semidefinite programming

D.D. Yao, S. Zhang and X.Y. Zhou

We solve stochastic linear quadratic control problems using a newly developed technique known as semidefinite programming. We further develop a software system through which a simple portfolio tracking a finacial index, such as the stock index or a steady growth rate, can be formed using this technique.

Integration of OLAP and multidimensional inter-Transaction mining

Today's markets are much more competitive and dynamic than ever. Business enterprises prosper or fail according to the sophistication and speed of their information systems, and their ability to analyze and synthesize information using those systems. Integration of On-Line Analytical Processing (OLAP) and data mining is a promising direction since it facilitates interactive exploratory data analysis. The objective of this project aims at integrating OLAP and multidimensional inter-transaction data mining for large financial multidimensional databases.

Knowledge-Based Chaotic Prediction for Financial Engineering Applications

Financial time series, such as stock prices, options and indices, are often highly erratic and contain complex behaviors which make their accurate prediction, even on a short-term basis, extremely difficult. Recently, Barahona and Poon's work (1996) on deterministic chaos detection has generated much interest in short-term prediction of nonlinear time series. Empirical study under a wide range of problem context accumulates a substantial amount of ad-hoc knowledge for its effective use. The proposed work is to extend the previous work of the principal investigator on a fuzzy expert system for technical analysis of stocks, by integrating knowledge based techniques with chaotic detection of financial time series. The field of using chaotic prediction in finance is still not fully developed, and there is much ground for further research and development in this novel integrated scheme. The effectiveness of the scheme would be evaluated with methodologies such as neural predictors and evolutionary computing.

Mining Streams of Financial Data and News

Financial market trends prediction is a technique to forecast market trend changes, which assists financial market participants to spot arbitrage opportunities for investment. Currently, most existing reported data mining studies for trend prediction focused on the time-series perspectives. However, there are numerous social factors that contribute to financial market trends prediction, but cannot be obtained from or represented in time-series data. First, in order to effectively predict market trends, one main objective of this project is to develop new data mining techniques that deal with two different types of data, namely financial data (time-series data or simply data) and news articles (textual data or simply text) concurrently. Second, stock market traders need to monitor tens of thousands of data/text sources coming as open-ended data/text streams in an on-line fashion, and need to analyze and make decisions based on the data/text streams they have received as soon as they can. We will study trend predictions by investigating the above two interrelated issues and finding associations among multiple data/text streams.

The Study of Convertible Bonds: Pricing and Optimal Strategies

The global and local markets of convertible bonds have been expanding rapidly over the last decade. Hong Kong, as a regional bond center, has been making efforts to improve its financial infrastructure and take the lead in regional cooperation, especially further the integration with China Mainland since 2003. Many mainland-based corporations issue the convertibles to foreign investors through Hong Kong.

In this research, we investigate the pricing problem of convertible bonds in the presence of early conversion, optimal capital structure and endogenous bankrupt, develop explanations for the empirical puzzles such as late conversion and negative stock returns around the call dates, and extend this work to a more realistic credit risk model with jump risk.