Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
A fusion model of HMM, ANN and GA for stock market forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Forecasting the volatility of stock price index
Expert Systems with Applications: An International Journal
Statistical fuzzy interval neural networks for currency exchange rate time series prediction
Applied Soft Computing
A bivariate fuzzy time series model to forecast the TAIEX
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Efficient prediction of exchange rates with low complexity artificial neural network models
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
A neural network with a case based dynamic window for stock trading prediction
Expert Systems with Applications: An International Journal
A distance-based fuzzy time series model for exchange rates forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network
Expert Systems with Applications: An International Journal
Forecasting stock market short-term trends using a neuro-fuzzy based methodology
Expert Systems with Applications: An International Journal
A new approach based on artificial neural networks for high order multivariate fuzzy time series
Expert Systems with Applications: An International Journal
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Forecasting model of global stock index by stochastic time effective neural network
Expert Systems with Applications: An International Journal
Forecasting tourist arrivals by using the adaptive network-based fuzzy inference system
Expert Systems with Applications: An International Journal
A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4.5
Expert Systems with Applications: An International Journal
A dynamic architecture for artificial neural networks
Neurocomputing
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
Stock indices prediction using radial basis function neural network
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Proceedings of the 5th Annual ACM Web Science Conference
Hi-index | 12.05 |
Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions. The models analysed are multi-layer perceptron (MLP), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) to extract new input variables. The comparison for each model is done in two view points: Mean Square Error (MSE) and Mean Absolute Deviate (MAD) using real exchange daily rate values of NASDAQ Stock Exchange index.