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Prediction of Stock Price using Particle Swarm Optimization Algorithm and Box-Jenkins Time Series | ||
| International Journal of Finance & Managerial Accounting | ||
| مقاله 3، دوره 2، شماره 7، دی 2017، صفحه 25-31 اصل مقاله (569.5 K) | ||
| نوع مقاله: Original Article | ||
| نویسندگان | ||
Shokrolah Khajavi  1؛ Fateme Sadat Amiri2
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| 1Professor of Accounting, Shiraz University, Shiraz, Iran (Corresponding Author) | ||
| 2Msc of Accounting, Shiraz University, Shiraz, Iran | ||
| چکیده | ||
| The purpose of this research is predicting the stock prices using the Particle Swarm Optimization Algorithm and Box-Jenkins method. In this way, the information of 165 corporations is collected from 2001 to 2016. Then, this research considers price to earnings per share and earnings per share as main variables. The relevant regression equation was created using two variables of earnings per share and price to earnings per share, and stock prices were predicted through particle swarm optimization algorithm in MATLAB. IBM SPSS was used to predict stock prices with Box-Jenkins time series. The Results indicate that particle swarm optimization algorithm with 4% error and Box-Jenkins time series with 19% error, have the potential to predict stock prices of companies. Moreover, PSO algorithm model predict stock prices more precisely than Box-Jenkins time series. Also by using EViews 7 software, the results of Wilcoxon-Mann Whitney statistics showed that PSO algorithm predicts the stock price more accurately | ||
| کلیدواژهها | ||
| Box-Jenkins Time Series؛ Earnings per Share؛ Particle Swarm Optimization (PSO) Algorithms؛ Price to Earnings Ratio؛ Stock Price | ||
| مراجع | ||
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1) Alborzi, M., Yaghoub Nejad, A., Maghsoudi, H. (2008). Application of Artificial Neural Network to predict the stock price index and cash returns. Emp Stud Finan Accou, 22, 119-127.
2) Jahankhani, A., Safarian, A. (2003). Stock market reaction to the estimated dividend per share in the Tehran Stock Exchange. J Financ Rese, 5(2), 7-23.
3) Kenedy, J., Eberhart, R. (1995). Particle swarm optimization in proc. IEEE International Conference on Neural Networks, 4, 1942-1948.
4) Karegowda, G., Kumari, S. (2013). Particle swarm optimization algorithm based k-means and fuzzy c-means clustering. Int J Adv Res in Com Sci & Soft Eng, 13(7), 448-451. 5) Lin, W., Lian, Z., Gu, X., Jiao, B. (2014). A local and global search combined particle swarm optimization algorithm and its convergence analysis. Math Pro in Eng 2014, Article ID 905712, http://dx.doi.org/10.1155/2014/905712.
6) Mehrani, S., Mehrani, K. (2003). The relationship between profitability ratios and stock returns in the Tehran Stock Exchange. Iranian Account & Audit Rev, 10(3), 93-105.
7) Nobel, J. (2008). Europe sugar policy crisis Agricultaral, Industrial and Economics Beetrout and Cane of Iranian Sugar Industry, 15, 1-30.
8) Oliveira, F., Nobre, C., Zarate, L. (2013). Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index–case study of PETR4 Petrobras Brazil. Ext Syss with Appl, 40 (18), 7596–7606.
9) Sharifi, E., Verij Kazemi, M., Tamandani, S. (2011). Improved detection capacity distributed generation units to improve power quality indices using the improved PSO algorithm. Iranian J Marine Sci & Tech, 56(56), 67-75.
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