Catálogo de publicaciones - libros

There is no believable risk map because of the tremendous imprecision of the risk assessment due to the incomplete-data set. To improve the probability estimation, the fuzzy set methodology was introduced into the area of risk assessment with respect to natural disasters. A fuzzy risk represented by a possibility-probability distribution, which is calculated by employing the interior-outer-set model, can represent the imprecision of risk assessments with a small sample. Thus, by using the fuzzy set methodology, we can provide a soft risk map which can accommodate the imprecision of risk assessment. Soft risk map can be adopted as a useful tool for the representation and reasoning of uncertainty of risk assessments due to incompleteness in real-world applications.

This paper proposes a multi-objectives decision-making method for loan portfolio based on the efficient frontier. This method is based on a multitargets decision-making model with two objective functions, one of which is to be maximized for return with regard to risk and the other minimized for risk with regard to return. We will use the method of geometry to solve this difficult problem. This method has three advantages. First, given the relative weights of each component, the risk of the portfolio, or the return of the portfolio, we can obtain the corresponding optimal loan portfolio. Second, the method is easy to understand and the calculation is simple, because it avoids reversing many matrices. Third, banks can pay adequate attention to casting credit to valuedclients and limited-clients. As a result, banks will be able to serve the debt needs of their clients well.

The concept of multi-factors detached coefficients is put forth and an evaluation method is given for customer’s credit in this paper. We also get the optimal multi-factors detached coefficients matrix by minimizing the estimate errors. According to this matrix, we get the scores vector of customer’s credit at n   factors, further the credit of the customers is evaluated and selected by commerce banks.

In this study, a novel hybrid AI system framework is developed by means of a systematic integration of artificial neural networks (ANN) and rulebased expert system (RES) with web-based text mining (WTM) techniques. Within the hybrid AI system framework, a fully novel hybrid AI forecasting approach with conditional judgment and correction is proposed for improving prediction performance. The proposed framework and approach are also illustrated with an example here.

This study presents a neural network & web-based decision support system (DSS) for foreign exchange (forex) forecasting and trading decision, which is adaptable to the needs of financial organizations and individual investors. In this study, we integrate the back-propagation neural network (BPNN)- based forex rolling forecasting system to accurately predict the change in direction of daily exchange rates, and the Web-based forex trading decision support system to obtain forecasting data and provide some investment decision suggestions for financial practitioners. This research reveals the structure of the DSS by the description of an integrated framework, and meantime we find that the DSS is integrated, user-oriented by its implementation, and practical applications reveal that this DSS demonstrates very high forecasting accuracy and its trading recommendations are reliable.

Many methodologies have been introduced to deal with project portfolio selection problem including some techniques that help to evaluate individual projects, or to select a portfolio among available projects. This paper, however, provides several XML-based schemes for building efficient and flexible project portfolio selection systems based on an integrated portfolio selection model proposed. In additional to provide an interactive and adaptable portfolio selection management framework for the organizations, we also stress the use of XML to standardize the data representation for developing Webbased decision support systems with heterogeneous data sources and multiple decision models.