Volume 17, Issue 4 (2-2021)                   JSDP 2021, 17(4): 89-102 | Back to browse issues page

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Pashaei Z, Dehkharghani R. Stock Market Modeling Using Artificial Neural Network and Comparison with Classical Linear Models. JSDP. 2021; 17 (4) :89-102
URL: http://jsdp.rcisp.ac.ir/article-1-939-en.html
Faculty of Engineering, University of Bonab
Abstract:   (317 Views)
Stock market plays an important role in the world economy. Stock market customers are interested in predicting the stock market general index price, since their income depends on this financial factor; Therefore, a reliable forecast in stock market can be extremely profitable for stockholders. Stock market prediction for financial markets has been one of the main challenges in forecasting financial time series, in recent decades. This challenge has increasingly attracted researchers from different scientific branches such as computer science, statistics, mathematics, and etc. Despite a good deal of research in this area, the achieved success is far from ideal. Due to the intrinsic complexity of financial data in stock market, designing a practical model for this prediction is a difficult task. This difficulty increases when a wide variety of financial factors affect the stock market index. In this paper, we attempt to investigate this problem and propose an effective model to solve this challenge. Tehran’s stock market has been chosen as a real-world case study for this purpose. Concretely, we train a regression model by several features such as first and second market index in the last five years, as well as other influential features including US dollar price, universal gold price, petroleum price, industry index and floating currency index. Then, we use the trained system to predict the stock market index value of the following day. The proposed approach can be used by stockbrokers-trading companies that buy and sell shares for their clients to predict the stock market value. In the proposed method, intelligent nonlinear systems such as Artificial Neural Networks (ANNs) and Adaptive Network-based Fuzzy Inference System (ANFIS) have been exploited to predict the daily stock  market value of Tehran’s stock market. At the end, the performance of these models have been measured and compared with the linear classical models, namely, ARIMA and SARIMA. In the comparison phase, these time series data are imposed to non-linear ANN and ANFIS models; then, feature selection is applied on data to extract the more influencing features, by using mutual information (MI) and correlation coefficient (CC) criteria. As a result, those features with greater impact on prediction are selected to predict the stock market value. This task eliminates irrelevant data and minimizes the error rate. Finally, all models are compared with each other based on common evaluation criteria to provide a big picture of the exploited models. The obtained results approve that the feature selection by MI and CC methods in both ANFIS and ANN models increases the accuracy of stock market prediction up to 55 percentage points. Furthermore, ANFIS could outperform ANN in all five evaluation criteria.
Full-Text [PDF 3708 kb]   (80 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2018/12/17 | Accepted: 2020/11/11 | Published: 2021/02/22 | ePublished: 2021/02/22

1. [1] B. K. Wong, T. A. Bodnovich, and Y. Selvi, "Neural network applications in business: A review and analysis of the literature (1988-1995)," Deciion Support Systems, vol. 19, no. 4, pp. 301-320, 1997. [DOI:10.1016/S0167-9236(96)00070-X]
2. [2] T. Kimoto, K. Asakawa, M. Yoda, and M. Takeoka, "Stock market prediction system with modular neural networks," 1990 IJCNN International Join. Conference on Neural Networks, vol.1, pp. 1-6, 1990. [DOI:10.1109/IJCNN.1990.137535]
3. [3] G. Tkacz, "Neural network forecasting of Canadian GDP growth," Internatıonal Journal of Forecastıng, vol. 17, no. 1, pp. 57-69, 2001. [DOI:10.1016/S0169-2070(00)00063-7]
4. [4] M.Mahdavi, H.Ahaki, B.Nasersharif. "Designing a Currency Recognition System Based on Neural Networks Using Texture and Color of Images," JSDP, 2011.
5. [5] G. A. Darbellay and M. Slama, "Forecasting the short-term demand for electricity," Internatıonal Journal of Forecastıng, vol. 16, no. 1, pp. 71-83, 2000. [DOI:10.1016/S0169-2070(99)00045-X]
6. [6] T. Hill, L. Marquez, M. O'Connor, and W. Remus, "Artificial neural network models for forecasting and decision making," Internatıonal Journal of Forecastıng, vol. 10, no. 1, pp. 5-15, 1994. [DOI:10.1016/0169-2070(94)90045-0]
7. [7] D. Enke and S. Thawornwong, "The use of data mining and neural networks for forecasting stock market returns," Expert Systems with Application., vol. 29, no. 4, pp. 927-940, 2005. [DOI:10.1016/j.eswa.2005.06.024]
8. [8] G. S. Atsalakis and K. P. Valavanis, "Forecasting stock market short-term trends using a neuro-fuzzy based methodology," Expert Systems with Application, vol. 36, no. 7, pp. 10696-10707, 2009. [DOI:10.1016/j.eswa.2009.02.043]
9. [9] E. Guresen, G. Kayakutlu, and T. U. Daim, "Using artificial neural network models in stock market index prediction," Expert Systems with Application, vol. 38, no. 8, pp. 10389-10397, 2011. [DOI:10.1016/j.eswa.2011.02.068]
10. [10] W. Qiu, X. Liu, and L. Wang, "Forecasting shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees," Expert Systems with Application, vol. 39, no. 9, pp. 7680-7689, 2012. [DOI:10.1016/j.eswa.2012.01.051]
11. [11] A. H. Moghaddam, M. H. Moghaddam, and M. Esfandyari, "Stock market index prediction using artificial neural network," Journal of Economics, Finance and Administrative Science, vol. 21, no. 41, pp. 89-93, 2016. [DOI:10.1016/j.jefas.2016.07.002]
12. [12] M. Qiu, Y. Song, and F. Akagi, "Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market," Chaos, Solitons & Fractals, vol. 85, pp. 1-7, 2016. [DOI:10.1016/j.chaos.2016.01.004]
13. [13] N. Tripathy, "Predicting Stock Market Price Using Neural Network Model," International Journal of Strategic Decision Sciences., vol. 9, no. 3, pp. 84-94, 2018. [DOI:10.4018/IJSDS.2018070104]
14. [14] Z. Hajirahimi and M. Khashei, "Hybrid structures in time series modeling and forecasting: A review," Engineering Applications of Artificial Intelligence, DOI: https://doi.org/10.1016/j.engappai.2019.08.018 [DOI:10.1016/j.engappai.2019.08.018 vol. 86, no.1, pp. 83-106, 2019.]
15. [15] D. Selvamuthu, V. Kumar, and A. Mishra, "Indian stock market prediction using artificial neural networks on tick data," Financial Innovation, vol. 5, no. 6, 2019. [DOI:10.1186/s40854-019-0131-7]
16. [16] J. Silva, J. V. Villa, and D. Cabrera, "Sale forecast for basic commodities based on artificial neural networks prediction," poceedings in Advances in Intelligent Systems and Computing, pp. 37-43, 2019. [DOI:10.1007/978-3-030-23887-2_5]
17. [17] S. Chopra, D. Yadav, and A. N. Chopra, "Artificial Neural Networks Based Indian Stock Market Price Prediction: Before and After Demonetization," Journal of Swarm Inteligence Evolutionary Computation, vol. 8, no. 174, pp. 2, 2019.
18. [18] Jadhav S., Dange B., Shikalgar S. "Prediction of Stock Market Indices by Artificial Neural Networks Using Forecasting Algorithms". International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol. 632, Springer, Singapore.
19. [19] T. Quoc Bao, L. Nhat Tan, L. Thi Thanh An, and B. Thi Thien My, "Forecasting stock index based on hybrid artificial neural network models," Science and Technology Deveelopment Journal - Economics - Law and Management, vol. 3, no. 1, 2019. [DOI:10.32508/stdjelm.v3i1.540]
20. [20] Ashik A.M., Kannan K.S. "Time Series Model for Stock Price Forecasting in India". In: Logistics, Supply Chain and Financial Predictive Analytics. Asset Analytics (Performance and Safety Management). Springer, Singapore, 2019 . [DOI:10.1007/978-981-13-0872-7_17]
21. [21] R. S. Pressman, "Software Engineering A Practitioner's Approach". 7th Ed. - Roger S. Pressman. 2009.
22. [22] J. D. Salas, "Applied modeling of hydrologic time series", Water Resources Publication, 1980. [DOI:10.1016/0309-1708(80)90028-7]
23. [23] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, "Time series analysis: forecasting and control", John Wiley & Sons, 2015.
24. [24] M. Alborzi, "An introduction to Neural Networks." Sharif University of Technology, 2000.
25. [25] S. Alvisi, G. Mascellani, M. Franchini, and A. Bárdossy, "Water level forecasting through fuzzy logic and artificial neural network approaches," Hydrology and Earth System Sciences, vol. 10. pp. 1-17, 2006. [DOI:10.5194/hess-10-1-2006]
26. [26] J. S. R. Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System," IEEE Transaction Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993. [DOI:10.1109/21.256541]

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