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In India financial markets have existed for many years. A functionallyaccented, diverse, efficient and flexible financial system is vital to thenational objective of creating a market driven, productive and competitiveeconomy. Today markets of varying maturity exist in equity, debt, commoditiesand foreign exchange. In this work we attempt to generate prediction rulesscheme for stock price movement at Bombay Stock Exchange using an importantSoft Computing paradigm viz., Rough Fuzzy Multi Layer Perception. The use ofComputational Intelligence Systems such as Neural Networks, Fuzzy Sets, GeneticAlgorithms, etc. for Stock Market Predictions has been widely established. Theprocess is to extract knowledge in the form of rules from daily stockmovements. These rules can then be used to guide investors. To increase theefficiency of the prediction process, Rough Sets is used to discretize thedata. The methodology uses a Genetic Algorithm to obtain a structured networksuitable for both classification and rule extraction. The modular concept,based on divide and conquer strategy, provides accelerated training and acompact network suitable for generating a minimum number of rules with highcertainty values. The concept of variable mutation operator is introduced forpreserving the localized structure of the constituting Knowledge Basedsub-networks, while they are integrated and evolved. Rough Set Dependency Rulesare generated directly from the real valued attribute table containing Fuzzymembership values. The paradigm is thus used to develop a rule extractionalgorithm. The extracted rules are compared with some of the related ruleextraction techniques on the basis of some quantitative performance indices.The proposed methodology extracts rules which are less in number, are accurate,have high certainty factor and have low confusion with less computation time.

Discovering Stock Price Prediction Rules of Bombay Stock Exchange Using Rough Fuzzy Multi Layer Perception Networks