Modeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms

Ajith Abraham · Ninan Sajith Philip · P. Saratchandran

67 citations0/0 files storedarXiv (Cornell University)

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Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

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 MarketS 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.

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References

2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index 2002Modified support vector machines in financial time series forecasting 2002Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support 2001Hybrid Intelligent Systems for Stock Market Analysis 2002Mining stock price using fuzzy rough set system 2002Daily stock market forecast from textual web data 1999Improving returns on stock investment through neural network selection 2002Stock market trading rule discovery using technical charting heuristics

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