S. Wang’s research while affiliated with Chinese Academy of Sciences and other places

In this study, a novel neural-network-based fuzzy group forecasting model is proposed for foreign exchange rates prediction. In the proposed model, some single neural network models are first used as predictors for foreign exchange rates prediction. Then these single prediction results produced by each single neural network models are fuzzified into a fuzzy prediction representation. Subsequently, these fuzzified prediction representations are aggregated into a fuzzy group consensus, i.e., aggregated prediction representation. Finally, the aggregated prediction representation is defuzzified into a crisp value as the final prediction results. For illustration and testing purposes, a typical numerical example and three typical foreign exchange rates prediction experiments are presented. Experimental results obtained reveal that the proposed neural network fuzzy group forecasting model can significantly improve the performance of foreign exchange rates forecasting.

A variable precision rough set (VPRS) model is used to solve the multi-attribute decision analysis (MADA) problem with multiple conflicting decision attributes and multiple condition attributes. By introducing confidence measures and a β-reduct, the VPRS model can rationally solve the conflicting decision analysis problem with multiple decision attributes and multiple condition attributes. For illustration, a medical diagnosis example is utilized to show the feasibility of the VPRS model in solving the MADA problem with multiple decision attributes and multiple condition attributes. Empirical results show that the decision rule with the highest confidence measures will be used as the final decision rules in the MADA problem with multiple conflicting decision attributes and multiple condition attributes if there are some conflicts among decision rules resulting from multiple decision attributes. The confidence-measure-based VPRS model can effectively solve the conflicts of decision rules from multiple decision attributes and thus a class of MADA problem with multiple conflicting decision attributes and multiple condition attributes are solved.

Financial crisis is a kind of typical rare event, but it is harmful to economic sustainable development if occurs. In this chapter, a Hilbert-EMD-based intelligent learning approach is proposed to predict financial crisis events for early-warning purpose. In this approach a typical financial indicator currency exchange rate reflecting economic fluctuation condition is first chosen. Then the Hilbert-EMD algorithm is applied to the economic indicator series. With the aid of the Hilbert-EMD procedure, some intrinsic mode components (IMCs) of the data series with different scales can be obtained. Using these IMCs, a support vector machine (SVM) classification paradigm is used to predict the future financial crisis events based upon some historical data. For illustration purposes, two typical Asian countries including South Korea and Thailand suffered from the 1997-1998 disastrous financial crisis experience are selected to verify the effectiveness of the proposed Hilbert-EMD-based SVM methodology.

Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties. © 2008 Springer-Verlag Berlin Heidelberg. All rights are reserved.

In this study, a triple-phase support vector machine based neural network ensemble model is proposed for exchange rates forecasting. In the first phase, many different single neural network models are generated. In the second phase, a conditional generalized variance minimization method is used to select the appropriate ensemble members. In the final phase, the support vector machine regression method is used for neural network ensemble for prediction purpose. For further illustration, two exchange rate series are used for testing. Empirical results obtained reveal that this novel neural network ensemble model can improve the performance of foreign exchange rates forecasting.