The study analyzes the family of regime switching GARCH neural network models, which allow the generalization of MS type RS-GARCH models to MS-GARCH-NN models by incorparating with neural network architectures with different dynamics and forecasting capabilities both in addition to the family of...

In this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been assessed. The methodology used in this study considered the short-term...

The future is unknown and uncertain, but there are ways to predict future events and reap the rewards safely. One such opportunity is the application of machine learning and artificial intelligence for stock market prediction. The stock market is turbulent, yet using artificial intelligence to...

In financial markets, the success of an investor depends on the quality of information and the speed of his decision. Therefore, an accurate prediction allows investors to make informed decisions about investments and reduce risks. Until today, various researches have been carried out in order...

Since stock markets are volatile, dynamic and complicated, forecasting stock market return is considered as a challenging task. Nevertheless, researchers have developed various linear and non linear methods for effective forecasting. Among these neural networks are most suitable for forecasting...

In this paper we focus on analyzing the predictive accuracy of three different types of forecasting techniques, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Singular Spectral Analysis (SSA), used for predicting chaotic time series data. These techniques...