| Due to the development of network technologies, business information today is more easily accessed, captured, and transferred over an information highway. This transformation process of business information requires quick and accurate interpretation of information, and to facilitate business decision making processes, decision support systems in the emerging market should support accurate, flexible, and timely characteristics of information to users.; This dissertation focuses on the accuracy dimension in key financial applications, with use of artificial neural networks (ANNs). Artificial neural network models are often classified into two distinctive training types, supervised or unsupervised. Previous pattern classification researchers in business have mostly used back-propagation (BP) networks. In this dissertation, the BP network (supervised) and the Kohonen self-organizing feature map (unsupervised) are together examined for their effectiveness and desirability in financial classification tasks. Bankruptcy prediction (two-group) and bond-rating (multi-group) are selected as testbeds. Statistical classification techniques, logistic regression and discriminant analysis, are also provided as performance benchmarks for neural network classifiers.; The findings of this study first confirmed that the back-propagation (BP) network outperformed all the other classification techniques used in this study. In addition, the study showed that as training sample size increased, a more complex BP model might be applied, and as a result, the performance of the BP network would improve accordingly. Second, Lowe and Webb's (1991) reciprocally weighted target coding scheme was empirically tested with two other target coding & threshold schemes. The Lowe and Webb scheme did not seem to work well. Third, the study identified a few key conditions for using the Kohonen self-organizing feature map in pattern classification settings. Provided that these key conditions were met, the Kohonen self-organizing feature map may be used as an alternative for pattern classification tasks. |
