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Prediction of stock market is a challenging task that has attracted researchers in various fields including the computational intelligence and finance. Since stock market data sets are intrinsically large, nonlinear and time-varying, it is extremely difficult to design models for forecasting the future directions with an acceptable accuracy. In this paper, an integrative and intelligent machine learning...
The synergy effect's benefit is widely accepted. The object of this paper is to investigate whether a hybrid approach combining different stock prediction approaches together can dramatically outperform the single approach and compare the performance of different hybrid approaches. The hybrid model includes three well-researched algorithms: back propagation neural network (BPNN), adaptive network-based...
In order to evaluate the performance of several combining forecasts, the paper firstly uses three single forecasting methods, namely grey model(GM (1,1)), BP neural networks and support vector machines (SVM), to forecast the Shanghai Industrial Index, the Shanghai Commercial Index, the Shanghai Real Estate Index, the Shanghai Public Utilities Index. Then it uses optimal weight linear combining forecasts...
CARRX model is a new volatility model. This paper applies least squares support vector regression to the CARRX model and a LSSVR-based CARRX model is established for predicting the range volatility of Chinese stock index. Out-of-sample forecasting results of using the LSSVR-CARRX model are compared with that of the ANN-CARRX model. Empirical results show that for the RMSE, MAE, MPE, Theil and Mincer-Zarnowitz...
In this paper, a hybrid prediction model based on rough set (RS) and support vector machine (SVM), RSS prediction model, is proposed to explore the stock index futures tendency. In this approach, RS is used for feature vectors selection to reduce the computation complexity of SVM and then the SVM is used to identify stock index futures movement direction. To evaluate the prediction ability of RSS...
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