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In this study, the authors discuss a least squares version of fuzzy support vector machine (FSVM) classifiers for designing a credit risk assessment system to discriminate good creditors from bad ones. Relative to the classical FSVM, the least squares FSVM (LS-FSVM) can transform a quadratic programming problem into a linear programming problem thus reducing the computational complexity. For illustration,...
In the mean-variance-skewness-kurtosis framework, this study solve multiple conflicting and competing portfolio objectives such as maximizing expected return and skewness and minimizing risk and kurtosis simultaneously, by construction of a polynomial goal programming (PGP) model into which investor preferences over higher return moments are incorporated. To examine its practicality, the approach...
In this study, we propose a novel nonlinear ensemble forecasting model integrating generalized linear auto-regression (GLAR) with artificial neural networks (ANN) in order to obtain accurate prediction results and ameliorate forecasting performances. We compare the new model's performance with the two individual forecasting models—GLAR and ANN—as well as with the hybrid model and the linear combination...
In this study, a hybrid artificial intelligent (AI) system integrating neural network and expert system is proposed to support foreign exchange (forex) trading decisions. In this system, a neural network is used to predict the forex price in terms of quantitative data, while an expert system is used to handle qualitative factor and to provide forex trading decision suggestions for traders incorporating...
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