Abstract
Many different time-series methods have been widely used in forecast stock prices for earning a profit. However, there are still some problems in the previous time series models. To overcome the problems, this paper proposes a hybrid time-series model based on a feature selection method for forecasting the leading industry stock prices. In the proposed model, stepwise regression is first adopted, and multivariate adaptive regression splines and kernel ridge regression are then used to select the key features. Second, this study constructs the forecasting model by a genetic algorithm to optimize the parameters of support vector regression. To evaluate the forecasting performance of the proposed models, this study collects five leading enterprise datasets in different industries from 2003 to 2012. The collected stock prices are employed to verify the proposed model under accuracy. The results show that proposed model is better accuracy than the other listed models, and provide persuasive investment guidance to investors.
Highlights
The prices forecast of stock is the most key issue for investors in the stock market, because the trends of stock prices are nonlinear and nonstationary time-series data, which makes forecasting stock prices a challenging and difficult task in the financial market
This paper proposes a novel genetic algorithm (GA)-support vector regression (SVR) time series model based on indicator selection to overcome these problems, and the proposed model contributes the following: (1) In feature selection, this study applies multivariate adaptive regression spline (MARS), stepwise regression (SR), and kernel ridge regression to get the key technical indicators for investors
This paper proposes a GA-SVR time series model based on feature selection to forecast the leading industry stock price
Summary
The prices forecast of stock is the most key issue for investors in the stock market, because the trends of stock prices are nonlinear and nonstationary time-series data, which makes forecasting stock prices a challenging and difficult task in the financial market. From the related work mentioned above, previous studies have shown some drawbacks: (1) Many researches select key technical indicators depending on experiences and ideas [23], (2) Most statistical methods follow some assumptions in different datasets, and obey the statistical distributions [3], (3) Most previous time series models consider only one feature to forecast stock indexes [23], and (4) The parameter of SVR is difficult to determine [24, 25, 26]. This paper proposes a novel GA-SVR time series model based on indicator selection to overcome these problems, and the proposed model contributes the following: (1) In feature selection, this study applies multivariate adaptive regression spline (MARS), stepwise regression (SR), and kernel ridge regression to get the key technical indicators for investors.
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