Title: A novel artificial immune system-based approach for mining associative classification rules with stock trading data
Authors: Mahsa Mahboob Ghodsi; M. Zandieh
Addresses: Department of Industrial Management, Management and Accounting Faculty, Allameh Tabataba'i University, Tehran, Iran ' Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran
Abstract: Stock market prediction with high accuracy has always been an interesting subject for most investors and professional analysts. Data mining techniques are providing great aid to extract interesting and hidden knowledge from datasets. Financial data mining tools assist investors in their investment decisions, thereby reducing their investment risks. Associative classification rule mining is a promising approach in data mining that utilises the association rule discovery techniques to construct classification systems, also known as associative classifiers. This paper aims to develop an intelligent transaction system based on associative classification rule mining (ACR) and phenotypic artificial immune system (AIS) which discovers trading rules from numerical indicators. A new fitness function as a different measure of quality for quantitative association is suggested considering interestingness of rules. Based on the empirical studies on the top eight companies in the S&P 500 stocks, observed results demonstrate the superior prediction accuracy over the genetic algorithm based technique and the 'buy and hold' strategy.
Keywords: stock market prediction; data mining techniques; associative classification rule mining; ACR; artificial immune system; AIS.
International Journal of Innovative Computing and Applications, 2017 Vol.8 No.3, pp.149 - 161
Received: 26 Apr 2016
Accepted: 27 Jun 2016
Published online: 15 Sep 2017 *