Shouyang Wang

Forecasting stock market movement direction with support vector machine

Support vector machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare its performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms the other classification methods. Further, we propose a combining model by integrating SVM with the other classification methods. The combining model performs best among all the forecasting methods.

Credit risk assessment with a multistage neural network ensemble learning approach

In this study, a multistage neural network ensemble learning model is proposed to evaluate credit risk at the measurement level. The proposed model consists of six stages. In the first stage, a bagging sampling approach is used to generate different training data subsets especially for data shortage. In the second stage, the different neural network models are created with different training subsets obtained from the previous stage. In the third stage, the generated neural network models are trained with different training datasets and accordingly the classification score and reliability value of neural classifier can be obtained. In the fourth stage, a decorrelation maximization algorithm is used to select the appropriate ensemble members. In the fifth stage, the reliability values of the selected neural network models (i.e., ensemble members) are scaled into a unit interval by logistic transformation. In the final stage, the selected neural network ensemble members are fused to obtain final classification result by means of reliability measurement. For illustration, two publicly available credit datasets are used to verify the effectiveness of the proposed multistage neural network ensemble model.

Credit risk evaluation with least square support vector machine

Credit risk evaluation has been the major focus of financial and banking industry due to recent financial crises and regulatory concern of Basel II. Recent studies have revealed that emerging artificial intelligent techniques are advantageous to statistical models for credit risk evaluation. In this study, we discuss the use of least square support vector machine (LSSVM) technique to design a credit risk evaluation system to discriminate good creditors from bad ones. Relative to the Vapniks support vector machine, the LSSVM can transform a quadratic programming problem into a linear programming problem thus reducing the computational complexity. For illustration, a published credit dataset for consumer credit is used to validate the effectiveness of the LSSVM

NEURAL NETWORKS IN FINANCE AND ECONOMICS FORECASTING

Artificial neural networks (ANNs) have been widely applied to finance and economic forecasting as a powerful modeling technique. By reviewing the related literature, we discuss the input variables, type of neural network models, performance comparisons for the prediction of foreign exchange rates, stock market index and economic growth. Economic fundamentals are important in driving exchange rates, stock market index price and economic growth. Most neural network inputs for exchange rate prediction are univariate, while those for stock market index prices and economic growth predictions are multivariate in most cases. There are mixed comparison results of forecasting performance between neural networks and other models. The reasons may be the difference of data, forecasting horizons, types of neural network models and so on. Prediction performance of neural networks can be improved by being integrated with other technologies. Nonlinear combining forecasting by neural networks also provides encouraging results.

Hybridizing exponential smoothing and neural network for financial time series predication

In this study, a hybrid synergy model integrating exponential smoothing and neural network is proposed for financial time series prediction. The proposed model attempts to incorporate the linear characteristics of an exponential smoothing model and nonlinear patterns of neural network to create a “synergetic” model via the linear programming technique. For verification, two real-world financial time series are used for testing purpose.

Neural network metalearning for credit scoring

In the field of credit risk analysis, the problem that we often encountered is to increase the model accuracy as possible using the limited data. In this study, we discuss the use of supervised neural networks as a metalearning technique to design a credit scoring system to solve this problem. First of all, a bagging sampling technique is used to generate different training sets to overcome data shortage problem. Based on the different training sets, the different neural network models with different initial conditions or training algorithms is then trained to formulate different credit scoring models, i.e., base models. Finally, a neural-network-based metamodel can be produced by learning from all base models so as to improve the reliability, i.e., predict defaults accurately. For illustration, a credit card application approval experiment is performed.

Credit risk analysis using a reliability-based neural network ensemble model

Credit risk analysis is an important topic in the financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. In this study, we try to use a triple-phase neural network ensemble technique to design a credit risk evaluation system to discriminate good creditors from bad ones. In this model, many diverse neural network models are first created. Then an uncorrelation maximization algorithm is used to select the appropriate ensemble members. Finally, a reliability-based method is used for neural network ensemble. For further illustration, a publicly credit dataset is used to test the effectiveness of the proposed neural ensemble model.

A double-stage genetic optimization algorithm for portfolio selection

In this study, a double-stage genetic optimization algorithm is proposed for portfolio selection. In the first stage, a genetic algorithm is used to identify good quality assets in terms of asset ranking. In the second stage, investment allocation in the selected good quality assets is optimized using a genetic algorithm based on Markowitzs theory. Through the two-stage genetic optimization process, an optimal portfolio can be determined. Experimental results reveal that the proposed double-stage genetic optimization algorithm for portfolio selection provides a very feasible and useful tool to assist the investors in planning their investment strategy and constructing their portfolio.

A novel nonlinear neural network ensemble model for financial time series forecasting

In this study, a new nonlinear neural network ensemble model is proposed for financial time series forecasting. In this model, many different neural network models are first generated. Then the principal component analysis technique is used to select the appropriate ensemble members. Finally, the support vector machine regression method is used for neural network ensemble. For further illustration, two real financial time series are used for testing.

A novel support vector machine metamodel for business risk identification

In this study, support vector machine (SVM) is used as a metamodeling technique to design a business risk identification system. First of all, a bagging sampling technique is used to generate different training sets. Based on the different training sets, different SVM models with different parameters, i.e., base models, are then trained to formulate different classifiers. Finally, a SVM-based metamodel (i.e., metaclassifier) can be produced by learning from all base models. For illustration the proposed metamodel is applied to a real-world business insolvency risk classification problem.