Performance Analysis of Connectionist Paradigms for Modeling Chaotic Behavior of Stock Indices Ajith Abraham 1 , Ninan Sajith Philip 2 , Baikunth Nath 3 , P. Saratchandran 4 1 Faculty of Information Technology, School of Business Systems, Monash University, Clayton, Victoria 3168, Australia, Email: ajith.abraham@ieee.org 2 Department of Physics, Cochin University of Science and Technology, Kerala 682022, India, Email: nsp@stthom.ernet.in 3 Department of Computer Science and Software Engineering. The University of Melbourne, Victoria 3010, Australia, Email: baikunth@unimelb.edu.au 4 School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Email: epsarat@ntu.edu.sg 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 SM 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. Key words: connectionist paradigm, support vector machine, neural network, difference boosting, neuro-fuzzy, stock market. 1. Introduction During the last decade, stocks and futures traders have come to rely upon various types of intelligent systems to make trading decisions [1]. Several intelligent systems have in recent years been developed for modelling expertise, decision support and complicated automation tasks etc. In this paper, we analysed the seemingly chaotic behaviour of two well-known stock indices namely Nasdaq-100 index of Nasdaq SM [9] and the S&P CNX NIFTY stock index [10]. Nasdaq-100 index reflects Nasdaq's largest companies across major industry groups, including computer hardware and software, telecommunications, retail/wholesale trade and biotechnology [9]. The Nasdaq-100 index is a modified capitalization-weighted index, which is designed to limit domination of the Index by a few large stocks while generally retaining the capitalization ranking of companies. Through an investment in Nasdaq-100 index tracking stock, investors can participate in the collective performance of many of the Nasdaq stocks that are often in the news or have become household names. Similarly, S&P CNX NIFTY is a well-diversified 50 stock index accounting for 25 sectors of the economy [10]. It is used for a variety of purposes such as benchmarking fund portfolios, index based derivatives and index funds. The CNX Indices are computed using market capitalisation weighted method, wherein the level of the Index reflects the total market value of all the stocks in the index relative to a particular base period. The method also takes into account constituent changes in the index and importantly corporate actions such as stock splits, rights, etc without affecting the index value.