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In this study, we propose a business intelligent model integrating econometric models, i.e. ARMA, GARCH, and ANN models for VaR estimation. The business intelligent model achieves better efficiency in input variables selecting because they are selected and newly created by time series models. Repetitive trial error process could be effectively eliminated to one time series process. On the other hand,...
Credit scoring has obtained more and more attention as the credit industry can benefit from reducing potential risks. Hence, many different useful techniques, known as the credit scoring models, have been developed by the banks and researchers in order to solve the problems involved during the evaluation process. In this paper, a hybrid credit scoring model (HCSM) is developed to deal with the credit...
In this study, we propose a least squares bilateral-weighted fuzzy support vector machine (LS-BFSVM) method to evaluate the credit risk problem. The method can not only reduce the computational complexity by considering equality constraints instead of inequalities for the classification problem with a formulation in least squares sense, but also increase the training algorithm's generalization ability...
This paper proposes a method to discover time series patterns predictive of the occurrence of specified events. The process of technique realization is mainly composed of four steps: specifying interest function and parameters; searching ODs, clustering ODs for candidate patterns and identifying patterns. A simulation study is conducted as verification. And an application study in the stock market...
Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, more accurate measures and better forecasts of volatility are crucial for the implementation and evaluation of asset and derivative pricing models in addition to trading and hedging strategies. However, whilst GARCH models are able to capture the observed clustering effect in asset price volatility, they...
In order to avoid the over-fitting in the training of neural networks, we apply Bayesian learning to neural networks. We illustrate the advantages of Bayesian learning by concentrating on multilayer perceptrons (MLP) neural networks and Markov Chain Monte Carlo (MCMC) method for computing the integrations. We conduct the experiments on the foreign exchange rate forecasting by using the approach. The...
To produce high quality recommendations and achieve high coverage in the face of data sparsity in recommender systems, we explore category-based adjusted conditional probability similarity (CACPS) collaborative filtering technique in this paper. CACPS technique firstly analyzes the user-item matrix to identify relationships between different items, and then uses these relationships to indirectly compute...
In SVM ensemble learning, diversity strategy is one of the most important determinants to obtain good performance. In order to examine and analyze the impacts of diversity strategies on SVM ensemble learning, this study tries to make such a deep investigation by taking credit scoring as an illustrative example. Experimental results found that the accuracy of ensemble models will be increased if ensemble...
This paper proposes a novel multi scale nonlinear ensemble methodology for analyzing and modeling the complex exchange rate behaviors. Using several techniques integrated under the proposed unified framework, it deals with data characteristics such as autocorrelation, multi scale heterogeneity and parameter instability during the modeling process. The multi scale heterogeneity property is modeled...
Hamming window function was applied to studying sampling theorem. A continuous band-limited spectrum function F(w) was constructed with Hamming window function. Its corresponding time-domain signal f(t) was worked out by inverse Fourier transform. f(t) was sampled with a comb function dT(t). By modifying the value of T, all kinds of sampling signals were produced, including critical, over and under...
Financial diagnosis is an important and widely studied topic in the last three decades. Recently, the support vector machine (SVM) has been applied to the problem of financial diagnosis. Fuzzy c-means clustering (FCM) is among considerable techniques for data reduction. In addition, principal component analysis (PCA) is a powerful technique for feather extraction. This paper proposes using fuzzy c-means...
Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. But bagging does not work very well in some case, such as k-nearest neighbor (kNN). At the same time, query learning strategies using bagging is also not work very well. From features view, we introduce bagging features active learning (ALBF) for kNN and apply...
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