Performance analysis of connectionist paradigms for modeling chaotic behavior of stock indices
Second international workshop on Intelligent systems design and application
Modeling chaotic behavior of stock indices using intelligent paradigms
Neural, Parallel & Scientific Computations - Special issue: Advances in intelligent systems and applications
A fusion model of HMM, ANN and GA for stock market forecasting
Expert Systems with Applications: An International Journal
Flexible neural trees ensemble for stock index modeling
Neurocomputing
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
Combining News and Technical Indicators in Daily Stock Price Trends Prediction
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
A type-2 fuzzy rule-based expert system model for stock price analysis
Expert Systems with Applications: An International Journal
Evolving and clustering fuzzy decision tree for financial time series data forecasting
Expert Systems with Applications: An International Journal
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Integrating Ensemble of Intelligent Systems for Modeling Stock Indices
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Grammar guided genetic programming for flexible neural trees optimization
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
The development of a weighted evolving fuzzy neural network
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Trend discovery in financial time series data using a case based fuzzy decision tree
Expert Systems with Applications: An International Journal
A hybrid fuzzy and neural approach for DRAM price forecasting
Computers in Industry
Stock price prediction based on procedural neural networks
Advances in Artificial Neural Systems
Evaluating direction-of-change forecasting: Neurofuzzy models vs. neural networks
Mathematical and Computer Modelling: An International Journal
A hybrid fuzzy intelligent agent-based system for stock price prediction
International Journal of Intelligent Systems
Trading team composition for the intraday multistock market
Decision Support Systems
A stock selective system by using hybrid models of classification
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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The use of intelligent systems for stock market predictions has been widely established. This paper deals with the application of hybridized soft computing techniques for automated stock market forecasting and trend analysis. We make use of a neural network for one day ahead stock forecasting and a neuro-fuzzy system for analyzing the trend of the predicted stock values. To demonstrate the proposed technique, we considered the popular Nasdaq-100 index of Nasdaq Stock MarketSM. We analyzed the 24 months stock data for Nasdaq-100 main index as well as six of the companies listed in the Nasdaq-100 index. Input data were preprocessed using principal component analysis and fed to an artificial neural network for stock forecasting. The predicted stock values are further fed to a neuro-fuzzy system to analyze the trend of the market. The forecasting and trend prediction results using the proposed hybrid system are promising and certainly warrant further research and analysis.