Chi-Jie Lu

CREDIT SCORING USING THE HYBRID NEURAL DISCRIMINANT TECHNIQUE

Abstract Credit scoring has become a very important task as the credit industry has been experiencing double-digit growth rate during the past few decades. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the decision of networks topology, importance of potential input variables and the long training process has often long been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring by integrating the backpropagation neural networks with traditional discriminant analysis approach. To demonstrate the inclusion of the credit scoring result from discriminant analysis would simplify the network structure and improve the credit scoring accuracy of the designed neural network model, credit scoring tasks are performed on one bank credit card data set. As the results reveal, the proposed hybrid approach converges much faster than the conventional neural networks model. Moreover, the credit scoring accuracies increase in terms of the proposed methodology and outperform traditional discriminant analysis and logistic regression approaches.

Financial time series forecasting using independent component analysis and support vector regression

As financial time series are inherently noisy and non-stationary, it is regarded as one of the most challenging applications of time series forecasting. Due to the advantages of generalization capability in obtaining a unique solution, support vector regression (SVR) has also been successfully applied in financial time series forecasting. In the modeling of financial time series using SVR, one of the key problems is the inherent high noise. Thus, detecting and removing the noise are important but difficult tasks when building an SVR forecasting model. To alleviate the influence of noise, a two-stage modeling approach using independent component analysis (ICA) and support vector regression is proposed in financial time series forecasting. ICA is a novel statistical signal processing technique that was originally proposed to find the latent source signals from observed mixture signals without having any prior knowledge of the mixing mechanism. The proposed approach first uses ICA to the forecasting variables for generating the independent components (ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables which contain less noise and served as the input variables of the SVR forecasting model. In order to evaluate the performance of the proposed approach, the Nikkei 225 opening index and TAIEX closing index are used as illustrative examples. Experimental results show that the proposed model outperforms the SVR model with non-filtered forecasting variables and a random walk model.

Mining the customer credit using classification and regression tree and multivariate adaptive regression splines

Credit scoring has become a very important task as the credit industry has been experiencing severe competition during the past few years. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the relative importance of potential input variables, long training process, and interpretative difficulties have often been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring using two commonly discussed data mining techniques-classification and regression tree (CART) and multivariate adaptive regression splines (MARS). To demonstrate the effectiveness of credit scoring using CART and MARS, credit scoring tasks are performed on one bank credit card data set. As the results reveal, CART and MARS outperform traditional discriminant analysis, logistic regression, neural networks, and support vector machine (SVM) approaches in terms of credit scoring accuracy and hence provide efficient alternatives in implementing credit scoring tasks.

Stock index prediction: A comparison of MARS, BPN and SVR in an emerging market

Stock index prediction seems to be a challenging task of the financial time series prediction process especially in emerging markets with their complex and inefficient structures. Multivariate adaptive regression splines (MARS) is a nonlinear and non-parametric regression methodology and has been successfully used in classification tasks. However, there are few applications using MARS in stock index prediction. In this study, we compare the forecasting performance of MARS, backpropagation neural network (BPN), support vector regression (SVR), and multiple linear regression (MLR) models in Shanghai B-Share stock index. Experimental results show that MARS outperforms BPN, SVR and MLR in terms of prediction error and prediction accuracy.

Sales Forecasting of IT Products Using a Hybrid MARS and SVR Model

In this study, a hybrid model using multivariate adaptive regression splines (MARS) and SVR is proposed for sales forecasting of information technology (IT) products. Support vector regression (SVR) has become a promising alternative for forecasting due to its generalization capability in obtaining a unique solution. However, one of the key problems is that SVR can not identify which forecasting variables are more important for building the forecasting model. For selecting an appropriate number of forecasting variables which can best improve the performance of the prediction model, a commonly discussed data mining technique, multivariate adaptive regression and splines (MARS), is adapted in this study. The proposed model first uses the MARS to select important forecasting variables. The obtained significant variables are then served as the inputs for the SVR model. Experimental results from three IT product sales data reveal that the obtained important variables from MARS can improve the forecasting performance of the SVR models. The proposed hybrid model outperforms the results using single SVR and single MARS models and hence provides an efficient alternative for IT product sales forecasting.

Forecasting stock price using Nonlinear independent component analysis and support vector regression

In developing a stock price forecasting model, the first step is usually feature extraction. Nonlinear independent component analysis (NLICA) is a novel feature extraction technique to find independent sources given only observed data that are mixtures of the unknown sources, without prior knowledge of the mixing mechanisms. It assumes that the observed mixtures are the nonlinear combination of latent source signals. This study propose a stock price forecasting model which first uses NLICA as preprocessing to extract features from forecasting variables. The features, called independent components (ICs), are served as the inputs of support vector regression (SVR) to build the prediction model. Experimental results on Nikkei 225 closing cash index show that the proposed method can produce the best prediction performance compared to the SVR models that use linear ICA, principal component analysis (PCA) and kernel PCA as feature extraction, and the single SVR model without feature extraction.

Predicting stock index using an integrated model of NLICA, SVR and PSO

Predicting stock index is a major activity of financial firms and private investors. However, stock index prediction is regarded as a challenging task of the prediction problem since the stock market is a complex, evolutionary, and nonlinear dynamic system. In this study, a stock index prediction model by integrating nonlinear independent component analysis (NLICA), support vector regression (SVR) and particle swarm optimization (PSO) is proposed. In the proposed model, first, the NLICA is used as preprocessing to extract features from observed stock index data. The features which can be used to represent underlying/hidden information of the original data are then served as the inputs of SVR to build the stock index prediction model. Finally, PSO is applied to optimize the parameters of the SVR prediction model since the parameters of SVR must be carefully selected in establishing an effective and efficient SVR model. Experimental results on Shanghai Stock Exchange composite (SSEC) closing cash index show that the proposed stock index prediction method is effective and efficient compared to the four comparison models.

Application of Independent Component Analysis Preprocessing and Support Vector Regression in Time Series Prediction

In this study, the application of independent component analysis (ICA), a new feature extraction method, and support vector regression (SVR) in time series prediction is presented. The proposed method first use ICA as preprocessing to transform the input space composed of original time series data into the feature space consisting of independent components (ICs) representing underlying information/features of the original data. Then, prediction models will be built by using SVR for ICs. Finally, the predicted value of each IC will be transformed back into the original space for time series prediction. Experimental results on the forecasting of NTD/USD exchange rate have showed that the proposed method outperforms the SVR model without ICA preprocessing.

Incorporating feature selection method into neural network techniques in sales forecasting of computer products

Sales forecasting of computer products is regarded as an important but difficult task since computer products are characterized by product variety, rapid specification changes and rapid price declines. Artificial neural networks (ANNs) have been found to be useful techniques for sales forecasting. However the inability to identify important forecasting variables is one of the main shortcomings of ANNs. For selecting an appropriate number of forecasting variables which can best improve the performance of the neural network prediction model, a commonly discussed data mining technique, multivariate adaptive regression and splines (MARS), is adapted in this study. The proposed model, firstly, uses the MARS to select important forecasting variables. The obtained significant variables are then served as the inputs for two neural network models-support vector regression (SVR) and cerebellar model articulation controller neural network (CMACNN). A real sales data collected from a Taiwanese computer dealer is used as an illustrative example. Experimental results showed that the obtained important variables from MARS can improve the forecasting performance of the SVR and CMACNN models. The proposed two-stage forecasting models provide good alternatives for sales forecasting of computer products.

Behavioral Scoring Model for Bank Customers Using Data Envelopment Analysis

This study proposes a behavior scoring model based on data envelopment analysis (DEA) to classify the customers into the high contribution and low contribution customers. Then, the low contribution customers are examined by using the slack analysis of DEA model to promote their contributions. The experiment results showed that the proposed method can provide indeed directions for bank to improve the contribution of the low contribution customers, and facilitates marketing strategy development.