This study presents an integrated system where wavelet transforms and recurrent neural network (RNN) based on artificial bee colony (abc) algorithm (called ABC-RNN) are combined for stock price forecasting. The system comprises three stages. First, the wavelet transform using the Harr wavelet is applied to decompose the stock price time series and thus eliminate noise. Second, the RNN, which ha...

Different techniques are available for the prediction of stock market. Very popular some of these are Neural Network, Data Mining, Hidden Markov Model(HMM) And Neuro-Fuzzy system. From these Neural Network and Neuro-Fuzzy Systems are the most leading machine learning techniques in stock market index prediction area. Other traditional methods do not cover all possible relation of stock price mov...

Financial markets all over the world have witnessed growing integration within as well as across boundaries, spurred by deregulation, globalization and advances in information technology. However, none of the researches have investigated the trading profitability of models that employed the financial market integration information as input variables especially in the case of day trading. Moreov...

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...

Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such be analyzed over time SPF. Machine learning deep have been shown to obtain better forecasts of than traditional approaches. This study, therefore, proposed a method enhance the perfo...