Evaluation of Artificial Immune System with Artificial Neural Network for Predicting Bombay Stock Exchange Trends
M. Gunasekaran and K. S. Ramaswami
DOI : 10.3844/jcssp.2011.967.972
Journal of Computer Science
Volume 7, Issue 7
Problem statement: The purpose of this study is to develop an artificial immune system for recognizing stock market trends and predict upward and downward directions of stock market. This study compared two prediction models, an Artificial Immune System (AIS) and Artificial Neural Network (ANN) for predicting the future index value, trend of Indian stock market and discovers the best prediction model. Approach: AIS is an efficient system for predicting trend due to its high capability of learning and retaining information in memory. Our proposed system was tested using SENSEX (Sensitive Index) data from Bombay Stock Exchange (BSE) of India. Results: Performance of models have been evaluated on the basis of the simulation results done on MATLAB. Experiments have been performed for both methods on well-known technical indicators and compared their results with SENSEX data. Conclusion: Artificial Immune System is more efficient than Artificial Neural Network.
© 2011 M. Gunasekaran and K. S. Ramaswami. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.