Intelligent Computational Methods for Financial Engineering

Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong As a multidisciplinary field, financial engineering is becoming increasingly important in today’s economic and financial world, especially in areas such as portfolio management, asset valuation and prediction, fraud detection, and credit risk management. For example, in a credit risk context, the recently approved Basel II guidelines advise financial institutions to build comprehensible credit risk models in order to optimize their capital allocation policy. Computational methods are being intensively studied and applied to improve the quality of the financial decisions that need to be made. Until now, computational methods and models are central to the analysis of economic and financial decisions. However, more and more researchers have found that the financial environment is not ruled by mathematical distributions or statistical models. In such situations, some attempts have also been made to develop financial engineering models using some emerging intelligent computing approaches. For example, an artificial neural network (ANN) is a non-parametric estimation technique which does not make any distributional assumptions regarding the underlying asset. Instead, ANN approach develops a model using sets of unknown parameters and lets the optimization routine seek the best fitting parameters to obtain the desired results. That is, these emerging intelligent solutions can efficiently improve the decisions for financial engineering problems. Having agreed on this basic fact, the guest editors determined that the main purpose of this special issue is not to merely illustrate the superior performance of a new intelligent computational method, but also to demonstrate how it can be used effectively in a financial engineering environment to improve and facilitate financial decision making. For this purpose, this special issue presents some new progress in intelligent computational methods for financial engineering In particular, the special issue addresses how the emerging intelligent computational methods (e.g., ANN, support vector machines, evolutionary algorithm, and fuzzy models etc.) can be used to develop intelligent, easy-to-use and/or comprehensible computational systems (e.g., decision support systems, agent-based system, and web-based systems etc.), which is expected to trigger some thoughts and deepen further research. In this special issue, 12 papers were selected from 26 submissions related to intelligent computational methods for financial engineering from different countries and regions. The authors of