Stock Price Prediction Modeling Using Artificial Neural Network Approach and Imperialist Competitive Algorithm Based On Chaos Theory

Document Type : Research Paper

Authors

1 Information technology and e-commerce engineering, electrical, computer and information technology, Azad University of Qazvin

2 Assistant Professor/Alzahra University

Abstract

Stock market is one of the options available to invest in liquidity. Investors in this area used a variety of approaches to predict stock prices. But due to the nonlinear relationship between variables affecting stock prices, Artificial Neural Networks are one of the most suitable approaches for this work. These networks, through different search optimization algorithms, try to identify the relationships between these variables. The higher the algorithms used, the higher the efficiency of the algorithms, the more accurate the identification of the relationships between the variables. In this paper, an attempt has been made to combine chaotic maps and colonial competition algorithms with the reform movement angle to the colonial colonies so that we can deal with the possibility of being trapped in local optimum to reduce as much as possible. Therefore, using this approach, it is tried to predict the stock price of Iran Khodro Company. To evaluate the performance of the proposed approach to other conventional approaches of neural network education, three perspectives: the degree of accuracy of prediction, the amount of memory used and the time of execution were used. The results show that the proposed approach has a better performance than other approaches.

Keywords

References

-      آتش پز گرگری ، اسماعیل " توسعه الگوریتم بهینه سازی اجتماعی و بررسی کارایی آن"، مهندسی برق-گرایش کنترل- دانشگاه تهران، 1387
-      باجلان ، سعید ؛ فلاحپور، سعید ؛ دانا، ناهید. " پیش بینی روند تغییرات قیمت سهم با استفاده از ماشین بردار پیشتیبان وزن دهی شده و انتخاب ویژگی هیبرید به منظور ارائه استراتژی معاملاتی بهینه" راهبرد مدیریت مالی 1395
-      منجمی، سید امیر حسین ؛ ابزری، مهدی ، شوازی، علیرضا رعیتی."پیش بینی قیمت سهام در بازار بورس اوراق بهادار با استفاده از شبکه ی عصبی فازی و الگوریتم های ژنتیک و مقایسه آن با شبکه ی عصبی مصنوعی، فصلنامه اقتصاد مقداری بررسی های اقتصادی سابق، 1388
-     Atashpaz Gargary Esmael. (2008). Development of Social Organizational Optimization Algorithm and its Performance Evaluation. Electrical Engineering - Control Taught - Tehran University(in persian).
-     Al-Radaideh, Qasem, A., & Adel Abu Alnagi, E. (2013). Predicting Stock Prices Using Data Mining Techniques. The International Arab Conference on Information Technology.
-     Ayodele, A. (2012). Stock Price Prediction using Neural Network with Hybridized Market Indicators. Journal of Emerging Trends in Computing and Information Sciences, 3, 2079-8407.
-     Bajelan, S., Falahpour, S.Dana,N. (2016). Projection of share price changes using a weighted support machine and selecting a hybrid feature to provide an optimal trading strategy. Financial Management Strategy(in persian).
-     Buza, K., Nagy, G. I., & Nanopoulos, A. (2014). Storage-optimizing clustering algorithms for high-dimensional tick data. Expert Systems with Applications, 41, 4148–4157.
-     Charles, A., & Darné, O. (2009). The random walk hypothesis for Chinese stock markets: Evidence from variance ratio tests. Economic Systems, 33, 117–126.
-     Hajizadeh, E., Ardakani, H. D., & Shahrab, J. (2010). Application of data mining techniques in stock markets:A survey. Journal of Economics and International Finance, 2, 109-118.
-     Lee, T.-S., & Chen, N.-J. (2002). Investigating the information content of non-cash-trading index futures using neural networks. Expert Systems with Applications, 22, 225–234.
-     Metghalchi, M., Chen, C.-P., & Hayes, L. A. (2015). History of share prices and market efficiency of the Madrid general stock index. International Review of Financial Analysis, 40, 178–184.
-     Monajemi, A., Abzari, M.Shavazi,A. (2009). Prognosis of Stock Price in the Stock Market Using Fuzzy Neural Network and Genetic Algorithms and Comparison with Artificial Neural Network. Quarterly Journal of Economics, Some ex-economic studies(in persian).
-     Park, Cheol-Ho; Irwin, Scott H. (2007). WHAT DO WE KNOW ABOUT THE PROFITABILITY OF TECHNICAL ANALYSIS? Journal of Economic Surveys, 21, 786–826.
-     Talatahari, S., Azar, B. F., Sheikholeslami, R., & Gandomi, A. (2012). Imperialist competitive algorithm combined with chaos for global optimization. Communications in Nonlinear Science and Numerical Simulation, 1312–1319.
-     Talatahari, S., Azar, B. F., Sheikholeslami, R., & Gandomi, H. (2012). Imperialist competitive algorithm combined with chaos. Commun Nonlinear Sci Numer Simulat, 17, 1312–1319.
-     Talatahari, S., Kaveh, A., & Sheikholeslami, R. (2012). Chaotic imperialist competitive algorithm for optimum design of truss structures. Structural and Multidisciplinary Optimization, 46, 355–367.
-     Talataharia, S., Azarb, B. F., Sheikholeslamib, R., & Gandomi, A. (2012). Imperialist competitive algorithm combined with chaos for global optimization. Communications in Nonlinear Science and Numerical Simulation, 1312–1319.
-     Ticknor, J. (2013). A Bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications, 40, 5501–5506.
-     Wang, & Jian-Zhou. (2011). Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38, 14346–14355.
Financial Management Strategy
Volume 5, Issue 3 - Serial Number 18
November 2017
Pages 27-73

Files

History

  • Receive Date: 13 March 2017
  • Revise Date: 04 November 2017
  • Accept Date: 23 October 2017

Share

How to cite

Statistics