Recently, many academy researchers have proposed several forecasting models by technical analysis to forecast stocks, such as (Yamawaki & Tokuoka 2007) [1]. The traditional approach uses a linear time series model for stock forecasting. However, the results would be in doubt when the forecasting problems are nonlinear. Multifeature data from financial statements usually produce high-dimensional...

forecasting stock price had been paying attention to many analysts and stockholders. today, this issue more recent years has been do by new methods but new methods, good enough, not have analysis of description and changes effective variables on stock price whereas all this method rely on regression bases. ardl (autoregression distributed lag ) of one equation cumulative regression method obtai...

The Vector Autoregressive (VAR) model, the Error Correction Model (ECM), and the Kalman Filter Model (KFM) are used to forecast UK stock prices. The forecasting performance of the three models is compared using out of sample forecasting. The results show that the forecasting performance of the ECM is better than that of the VAR and the KFM, and that the VAR performs a forecasting better than th...

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investi...

modeling and analysis of future prices has been hot topic for economic analysts in recent years. traditionally, the complex movements in the prices are usually taken as random or stochastic process. however, they may be produced by a deterministic nonlinear process. accuracy and efficiency of economic models in the short period forecasting is strategic and crucial for business world. nonlinear ...

Stock market prediction with data mining techniques is one of the most important issues to be investigated. In this paper, we present a system that predicts the changes of stock trend by analyzing the influence of non-quantifiable information (news articles). In particular, we investigate the immediate impact of news articles on the time series based on the Efficient Markets Hypothesis. Several...

The application of fuzzy time series models to forecasting has been drawing a great amount of attention. To provide a more sophisticated model to handle real world problems thus becomes important. This study intends to model fuzzy time series with multiple observations at a single time point. The proposed model shows how to fuzzify multiple observations into a fuzzy set. Neural networks are app...

In the past two decades, many forecasting models based on the concepts of fuzzy time series have been proposed for dealing with various problem domains. In this paper, we present a novel model to forecast enrollments and the close prices of stock based on particle swarm optimization and generalized fuzzy logical relationships. After that some concepts of the generalized fuzzy logical relationsh...

During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in forecasting. Now, there is a question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison betw...