Background/Objectives: To design a stock market prediction system using neuro-genetic approach. To predict BSE Sensex closing price using an artificial neural network. To optimize the synaptic weight values using genetic algorithm. Methods/Statistical Analysis: In this research work input parameters related to the BSE Sensex are fed as input dataset to the Multi Layer Perceptron neural network and the future day Sensex closing value is predicted as the output. Various training algorithms are implemented and the results are compared. The best neural network model is further is subjected to synaptic weight optimization using Genetic Algorithm. The various models are then subjected to testing over a period of 15 days, to obtain the most accurate model. Findings: The proposed system applies variants of Back Propagation (BP) learning algorithm on a Multi Layer Perceptron network (MLP) which is trained using four years' BSE Sensex data. The performance of the network is measured by Normalized Mean Squared Error (NMSE). It is observed that resilient back propagation algorithm with log sigmoid activation function gives the lowest NMSE of 0.003745. The research work also uses Genetic Algorithm (GA) for weight optimization. BP suffers from the danger of getting stuck in local minima. This is avoided by using GA to select the best synaptic weights and node thresholds initially and then proceeding with the training of MLP using BP. It is observed that this hybrid model gives optimized results. In order to substantiate the model proposed, experiments are first conducted without using GA. The results of this general BP MLP model are then compared with that of GA-BP MLP model and analyzed. NMSE for the GA-BP MLP model is 0.003092121. Artificial Neural Network has evolved out to be a better technique in capturing the structural relationship between a stock's performance and its determinant factors more accurately than many other statistical techniques. Although neural network models are used for stock prediction very little work is done on BSE SENSEX data. The proposed work is unique as experiments are done using different variants of back propagation learning algorithm and different activation functions. Therefore these experimental results add value to the existing work. The proposed work also demonstrates that performance improvement can be achieved by using genetic algorithm. Application/Improvements: The proposed model can also be used for forecasting index returns of markets like New York Stock Exchange, Hang Seng Stock Exchange, Korea Stock Exchange, Taiwan Stock Exchange etc., by using appropriate data set.
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