Stock price prediction using data mining algorithms in the Iranian stock market

Reyhaninezhad'Alla, Mohamad; Ghane, Saeid; Salehi, Allah Karam

doi:10.22075/ijnaa.2023.30809.4502

Stock price prediction using data mining algorithms in the Iranian stock market

Document Type : Research Paper

Authors

Mohamad Reyhaninezhad'Alla ¹

Saeid Ghane ²

Allah Karam Salehi ³

¹ Department of Management, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran.

² Department of Management, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran

³ Department of Accounting, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran

10.22075/ijnaa.2023.30809.4502

Abstract

Uncertainty in the capital market means the difference between the expected values and the values that occur in reality. The design of different analysis and forecasting methods in the capital market is also due to the high value and the need to know prices in the future with more certainty or less uncertainty. To earn profit in the capital market, investors have always sought to find the right share for investment and the right price for buying and selling, and therefore all the forecasting models proposed have always sought to answer three basic questions; What share, in what time frame and at what price should be bought or sold. In this article, we will use the combined method based on LSTM and a neural fuzzy system to predict stock prices in the Iranian market. The results show that the proposed method has an accuracy of over 90% in stock price prediction.

Keywords

Stock market

neural fuzzy system

lstm neural network

[1] N. Sran and N. Kaur, Comparative analysis of existing load balancing techniques in cloud computing, Int. J. Engin. Sci. Inv. 2 (2013), no 1.

[2] A. Khetan, V. Bhushan, and S. Chand Gupta, A novel survey on load balancing in cloud computing, Int. J. Engin. Res. Technol. 3 (2013), no. 2.

[3] Y. Ranjith Kumar, M. Madhu Priya, and K. Shahu Chatrapati, Effective distributed dynamic load balancing for the clouds, Int. J. Engin. Res. Technol. 2(2013), no. 2.

[4] S. Asadzadeh and M.A. Shayegan, Diagnosis of atherosclerosis of the coronary arteries of the heart with data
mining and machine learning techniques, Trans. Machine Intell. 6 (2023), no. 4, 198–207.

[5] S. Begum and C.S.R. Prashanth, Review of load balancing in cloud computing, Int. J. Comput. Sci. Iss. 10 (2013), no. 1, 343.

[6] A. Hamo and A. Saeed, Towards a reference model for surveying a load balancing, Int. J. Comput. Sci. Network Secur.13 (2013), no. 2, 42.

[7] D. Rastogi, A. Bansal, and N. Hasteer, A techniques of load balancing in cloud computing: A Survey, 7th Int. Conf. Comput. Sci. Engineering (CSE), April, 2013.

[8] N.J. Kansal and I. Chana, Cloud load balancing techniques: A step towards green computing, Int. J. Comput. Sci. Iss. 9 (2012), no. 1.

[9] A.A. Rajguru and S.S. Apte, A comparative performance analysis of load balancing algorithms in distributed system using qualitative parameters, Int. J. Recent Technol. Engin. 1 (2012), no. 3, 175–179.

[10] K. Al Nuaimi, N. Mohamed, M. Al Nuaimi, and J. Al-Jaroodi, A survey of load balancing in cloud computing: Challenges and algorithms, IEEE Second Symp. Network Cloud Comput. Appl., 2012.

[11] H. Ehsani Chimeh and M. Karami, Spam detection from big data based on evolutionary data mining systems, Trans. Machine Intell. 1 (2018), no. 1, 1–9.

[12] H. Avini, Z. Mirzaei Zavard Jani, and A. Avini, Improved recommender systems using data mining, Trans. Machine Intell. 5 (2022), no. 2, 97–114.