We propose a new computational method of input selection for stock market forecasting with neural networks. The method results from synthetically considering the special feature of input variables of neural networks and the special feature of stock market time series. We conduct the experiments to compare the prediction performance of the neural networks based on the different input variables b...

Artificial neural networks (ANN) is an approach to solving different tasks. In this paper we forecast U.S. stock market movements using two types of artificial neural networks: a network based on the Levenberg-Marquardt learning mechanism and a synergetic network which was described by German scientist Herman Haken. The LevenbergMarquardt ANN is widely used for forecasting financial markets, wh...

In this study, a hybrid intelligent data mining methodology, genetic algorithm based support vector machine (GASVM) model, is proposed to explore stock market tendency. In this hybrid data mining approach, GA is used for variable selection in order to reduce the model complexity of SVM and improve the speed of SVM, and then the SVM is used to identify stock market movement direction based on th...

Modeling and forecasting volatility of capital markets has been important area of inquiry and research in financial economics with the recognition of time-varying volatility, volatility clusturing, and asymmetric response of volatility to market movements. Given the anticipated growth of the Nepalese stock market and increasing interest of investors towards investment in Nepalese stock market, ...

Currently, stock price forecasting is carried out using either time series prediction methods or trend classifiers. The trend classifiers are designed to predict the behaviour of stock price’s movement. Recently, soft computing methods, like support vector machines, have shown promising results in the realization of this particular problem. In this paper, we apply several prototype generation c...

Stock market prediction is important and of great interest because successful prediction of stock prices may promise attractive benefits. These tasks are highly complicated and very difficult. In this paper, we investigate the predictability of stock market return with Adaptive Network-Based Fuzzy Inference System (ANFIS). The objective of this study is to determine whether an ANFIS algorithm i...

Uncertainty in the capital market means the difference between the expected values ​​and the amounts that actually occur. Designing different analytical and forecasting methods in the capital market is also less likely due to the high amount of this and the need to know future prices with greater certainty or uncertainty. In order to capitalize on the capital market, investors have always sough...

Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box–Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is propo...

In this paper, previous studies featuring, Machine learning based stock market analysis and predictions have been reviewed. This study is done to examine the various methodologies used in analyzing stock market data, and methods used prediction and forecasting the market. We propose a methodology that can be used in forecasting time series market data. Also we study the data sources. We have ex...