No abstract

No abstract

E-commerce (EC) is sweep across the globe and has become a most important commercial activity. Accordingly, EC also causes the academia's research interests. A lot of research achievements have been gained in recent years. This paper takes these achievements as research object and collects 8488 research papers published in academic journals during 2000-2013 included in Web of Science database. Using text mining techniques, 68 terms are identified as the main keywords of EC field. Then the scientific structure of the EC is mapped through multidimensional scaling, based upon the cooccurrence of the main terms in the academic journals. The results show that the EC domain is composed of three main fields, such as technology, management and customer. Furthermore, knowledge graph based on the EC research network is visualized and it shows that the whole EC research papers covering seven important subnets, which are: internet, consumer behaviour, customer satisfaction, online shopping, reputation, Taiwan and knowledge management.

Credit scoring and behavioral scoring have become very important credit risk management tasks during the past few years due to the impact of several financial crises. The objective of the proposed study is to explore the performance of behavioral scoring using three commonly discussed data mining techniqueslinear discriminant analysis (LDA), backpropagation neural networks (BPN), and support vector machine (SVM). To demonstrate the effectiveness of behavioral scoring using the above-mentioned techniques, behavioral scoring tasks are performed on one bank credit card dataset in Taiwan. As the results reveal, BPN outperforms other techniques in terms of overall scoring accuracy and hence is an efficient alternative in implementing behavioral scoring tasks.

In this paper, a time series forecasting approach by integrating particle swarm optimization (PSO) and support vector regression (SVR) is proposed. SVR has been widely applied in time series predictions. However, no general guidelines are available to choose the free parameters of an SVR model. The proposed approach uses PSO to search the optimal parameters for model selections in the hope of improving the performance of SVR. In order to evaluate the performance of the proposed approach, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) closing cash index is used as the illustrative example. Experimental results show that the proposed model outperforms the traditional SVR model and provides an alternative in financial time series forecasting.