Time series forecasting, such as stock price prediction, is one of the most important complications in the financial area as data is unsteady and has noisy variables, which are affected by many factors. This study applies a hybrid method of Genetic Algorithm (GA) and Artificial Neural Network (ANN) technique to develop a method for predicting stock price and time series. In the GA method, the output values are further fed to a developed ANN algorithm to fix errors on exact point. The analysis suggests that the GA and ANN can increase the accuracy in fewer iterations. The analysis is conducted on the 200-day main index, as well as on five companies listed on the NASDAQ. By applying the proposed method to the Apple stocks dataset, based on a hybrid model of GA and Back Propagation (BP) algorithms, the proposed method reaches to 99.99% improvement in SSE and 90.66% in time improvement, in comparison to traditional methods. These results show the performances and the speed and the accuracy of the proposed approach.
The use of computational intelligence systems such as neural networks, fuzzy set, genetic algorithms, etc., for stock market predictions has been widely established. This paper presents a generic stock pricing prediction model based on a rough set approach. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. The rough set reduction technique is applied to find all the reducts of the data which contains the minimal subset of attributes that are associated with a class label for prediction. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. Using a data set consisting of the daily movements of a stock traded in Kuwait Stock Exchange, a preliminary assessment indicates that rough sets are shown to be applicable and is an effective tool to achieve this goal. For comparison, the results obtained using the rough set approach were compared to that of the neural networks algorithm and it was shown that the Rough set approach has a higher overall accuracy rate and generates more compact and fewer rules than the neural networks.