Prediction of Financial Performance using Genetic Algorithm and Association Rule Mining

The proposed system introduces a new genetic algorithm for prediction of financial performance with input data sets from a financial domain. The goal is to produce a GA-based methodology that builds an associative classifier from numerical data. However, since issues may differ when dealing with various types of numerical data, this work restricts the numerical data to stock trading data. Along with genetic algorithm association rule mining algorithm is used for generation of association rules among the various Technical Indicators. These Association Rules are given as an input to the optimized output obtained from the genetic algorithm. The outcome of the above approach is being applied to a classification process in order to predict the class of the new inputs. The proposed idea in the project is to offer an efficient genetic algorithm in combination with the association rule mining algorithm which produces stock market forecasts.