Discovering Stock Price Prediction Rules of Bombay Stock Exchange Using
  Rough Fuzzy Multi Layer Perception Networks
  In India financial markets have existed for many years. A functionally
accented, diverse, efficient and flexible financial system is vital to the
national objective of creating a market driven, productive and competitive
economy. Today markets of varying maturity exist in equity, debt, commodities
and foreign exchange. In this work we attempt to generate prediction rules
scheme for stock price movement at Bombay Stock Exchange using an important
Soft Computing paradigm viz., Rough Fuzzy Multi Layer Perception. The use of
Computational Intelligence Systems such as Neural Networks, Fuzzy Sets, Genetic
Algorithms, etc. for Stock Market Predictions has been widely established. The
process is to extract knowledge in the form of rules from daily stock
movements. These rules can then be used to guide investors. To increase the
efficiency of the prediction process, Rough Sets is used to discretize the
data. The methodology uses a Genetic Algorithm to obtain a structured network
suitable for both classification and rule extraction. The modular concept,
based on divide and conquer strategy, provides accelerated training and a
compact network suitable for generating a minimum number of rules with high
certainty values. The concept of variable mutation operator is introduced for
preserving the localized structure of the constituting Knowledge Based
sub-networks, while they are integrated and evolved. Rough Set Dependency Rules
are generated directly from the real valued attribute table containing Fuzzy
membership values. The paradigm is thus used to develop a rule extraction
algorithm. The extracted rules are compared with some of the related rule
extraction 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.
