Financial researchers must identify the key technical indicators that have higher relevance to the stock price by indicator selection. Therefore, selecting the relevant indicators to forecast stock prices is one of the important issues for investors. However, choosing unrepresentative indicators may result in losing profits for investors. Investors usually prefer to select technical indicators depending on their experience or feelings for forecasting stock prices despite this behavior be highly risky. How to select the key variables from numerous technical indicators is a critical step in the forecasting process. In practice, researchers use many technical indicators as independent variables for forecasting stock prices. Therefore, conventional time series methods are not suitable for forecasting stock prices, because stock price fluctuation is usually nonlinear and nonstationary.įurther, most conventional time-series models utilize one variable (the previous day’s stock price) only, when, there are actually many influential factors, such as market indexes, technical indicators, economics, political environments, investor psychology, and the fundamental financial analysis of companies that can influence forecasting performance. Statistical methods, such as traditional time series models, usually address linear forecasting models and variables must obey statistical normal distribution. The most well-known conventional time series forecasting approach is autoregressive integrated moving average (ARIMA), which is employed when the time-series data is linear and there are no missing values. Conventional time series models have been used to forecast stock prices, and many researchers are still devoted to the development and improvement of time-series forecasting models. 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. The results show that proposed model is better accuracy than the other listed models, and provide persuasive investment guidance to investors. The collected stock prices are employed to verify the proposed model under accuracy. To evaluate the forecasting performance of the proposed models, this study collects five leading enterprise datasets in different industries from 2003 to 2012. Second, this study constructs the forecasting model by a genetic algorithm to optimize the parameters of support vector regression. 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. 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. However, there are still some problems in the previous time series models. Many different time-series methods have been widely used in forecast stock prices for earning a profit.
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