Comparison of single and ensemble support vector regression model for stock price prediction

Articles in Press

Document Type : Original Article

Authors

1 Department of Mathematics, Ayatollah Boroujerdi University, Boroujerd, Iran

2 Dept. of Economics, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Capital accumulation and proper asset management play a crucial role in the economic growth of countries. The significant impact of this factor, as well as its importance, can be clearly observed in the system of most countries, especially capitalist systems. The capital market is one of the most appropriate places to attract small assets and use them in order to grow and improve a company and also increase the personal assets of investors. Therefore, correct investment helps individuals to manage the surplus of their incomes properly, enabling them to attain the necessary capital to achieve their goals. Essentially, stock prices are nonlinear and chaotic, hence investing in the stock market carries a high risk. To minimize this risk and reduce hazards, an efficient system is required that can predict future stock price movements with high accuracy. Given the importance of this issue, machine learning models have shown suitable performance in this area. Machine learning algorithms have the ability to model nonlinear and complex systems. In this research, a novel collective approach based on Support Vector Regression constructs a composite or hybrid model that enjoys high accuracy and speed in predicting stock prices. The stocks used in this research are the ten index-making shares of FOLD, TAMN, PKLJ, MSMI, PNES, PTEH, PNBA, IKCO, SIPA and PARS, considered in the period from 2021-03-27 to 2024-07-21. Based on various criteria, the results demonstrate the superiority of the collective Support Vector Regression method over the conventional Support Vector Regression method and the Random Forest approach.

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Main Subjects

Soft Computing Journal

Articles in Press, Accepted Manuscript
Available Online from 07 March 2025

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History

  • Receive Date: 18 August 2024
  • Accept Date: 28 February 2025

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