Modeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms
  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.
