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Modeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms (2003)
| Content Provider | CiteSeerX |
|---|---|
| Author | Saratchandran, P. Philip, Ninan Sajith Abraham, Ajith |
| Abstract | The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techniques, we considered Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index. We analyzed 7 year's Nasdaq 100 main index values and 4 year's NIFTY index values. This paper investigates the development of a reliable and efficient technique to model the seemingly chaotic behavior of stock markets. We considered an artificial neural network trained using Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno neuro- fuzzy model and a Difference Boosting Neural Network (DBNN). This paper briefly explains how the different connectionist paradigms could be formulated using different learning methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the connectionist paradigms considered could represent the stock indices behavior very accurately. |
| File Format | |
| Publisher Date | 2003-01-01 |
| Publisher Institution | International Journal of Neural, Parallel & Scientific Computations, USA, Volume 11, Issue |
| Access Restriction | Open |
| Subject Keyword | Experiment Result Takagi-sugeno Neuro Fuzzy Model Reliable Forecast Model Required Level Connectionist Paradigm Cnx Nifty Stock Index Support Vector Machine Stock Market Index Nasdaq Stock Market Efficient Technique Intelligent System Stock Market Prediction Levenberg-marquardt Algorithm Main Index Value Stock Market Nifty Index Value Chaotic Behavior Nasdaq-100 Index Difference Boosting Neural Network Stock Index Using Intelligent Paradigm Paper Briefly Different Technique Stock Index Artificial Neural Network Different Connectionist Paradigm Several Connectionist Paradigm |
| Content Type | Text |
| Resource Type | Article |
