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Credit scoring model development became a very important issue as the credit industry has many competitions. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicantpsilas credit score from the applicantpsilas...
Stock return forecast has been an important issue and difficult task for both shareholders and financial professionals. To tackle this problem, we introduce least square support vector machine (LS-SVM), an improved algorithm that regresses faster than standard SVM, and dynamic inertia weight particle swarm optimization (W-PSO), that outperform standard PSO in parameter selection. The work of this...
The evaluation of competitive power is very important for bidder in power system, how to improve the accuracy and efficiency of evaluation is the keystone people pay attention to, and many researches have been done around it. A combined model of least squares support vector machines optimized by an improved particle swarm optimization algorithm is proposed in this paper to do evaluate the competitive...
[Purpose] Mass incidents have emerged as a serious social problem concerning national security in China. So, it is necessary to construct a forecasting model to predict such public events. In this paper, support vector machines are applied to the model. [Method] Based on the social surveys conducted in 119 counties of Shanxi, Gansu and Hubei provinces, 3 multi-class classification problems were proposed,...
In this paper, mixture models are used to classify documents. The basic assumption for the documents in a collection is that each class is composed of a number of mixture components. By identifying the components in the document collection, the classes of documents can thereby be identified from each other. A semi-supervised clustering method is proposed to identify the components (clusters), and...
The number of variables used for credit scoring can be quite large, and selecting the most relevant variables becomes an important topic. In this paper, we use gradient learning method for variable selection in credit scoring. The original method in the literature does not work on credit datasets because of the large sample size. To conquer this, we modify the algorithm by resampling data and voting...
Due to the fluctuation and complexity of the financial time series, it is difficult to use any single artificial technique to capture its non-stationary property and accurately describe its moving tendency. So a novel hybrid intelligent forecasting model based on empirical mode decomposition (EMD) and support vector regression (SVR) is proposed. EMD can adaptively decompose the complicated raw data...
The term structure models of interest rate have been extensively applied to asset pricing, design of financial instruments, hedging, arbitraging and investment decision. Therefore, the estimation of parameters in the term structure model has been a key problem. In this paper, the parameters of term structure model is estimated by using two different kernel functions: Gauss kernel function and Epanechnikov...
We apply a new bootstrap statistical technique to distinguish between dasiaskillpsila and dasialuckpsila for individual funds. This methodology allows for non-normality in the idiosyncratic risk of the funds-a major issue when considering those funds which appear to be either very good or very bad performers, since these are the funds which investors are primarily interested in identifying. We find...
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