System Engineering and Productivity

System Engineering and Productivity

A Novel Model Based on the ARIMA Model in Predicting Stock Prices of Tehran Stock Exchange Companies

Articles in Press

Document Type : Research Paper

Authors
Mansoureh Naderipour 1
Hadis Al-Sadat Hosseini 2
Mohammad-Bagher Jamali 3
1 Corresponding author: Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
2 B.Sc., Department of Industrial Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
3 Assistant Professor, Faculty of Industrial Engineering, Shiraz University of Technology, Shiraz, Iran
10.22034/sep.2025.2075221.1411
Abstract
In recent years, statistical time series models—particularly the ARIMA model—have been widely used as effective tools for stock price forecasting. Despite its acceptable accuracy, the model’s sensitivity to the number of historical data points remains a major limitation in practical applications. This study aims to enhance prediction accuracy and stability by proposing a new model called RARIMA (Reset Auto Regressive Integrated Moving Average), developed based on the ARIMA framework. The proposed model employs a Fast-Learning Reset approach to reduce the effect of the selected historical data length. Stock price data from 18 companies across four industries—steel, petrochemical, banking, and automotive—were analyzed over the period April 2018 to January 2024. To evaluate model performance, the Mean Relative Error (MRE) and the TOPSIS ranking method were used to compare short-term and long-term forecasting accuracy. The results indicate that the RARIMA model improves prediction accuracy by up to 75% compared to the traditional ARIMA model, while significantly reducing its sensitivity to the size of historical data. Accordingly, the proposed model can serve as a reliable and efficient tool for financial analysts and decision-makers in the capital market.

Highlights

  • Proposing a new model named RARIMA, based on the ARIMA framework, for forecasting stock prices in the Tehran Stock Exchange.
  • Applying the TOPSIS ranking method to compare forecasting accuracy across short-term and long-term time horizons.
  • Introducing the RARIMA model with a Fast-learning Reset approach, which significantly reduces the model’s sensitivity to the number of historical data points.
  • Achieving up to 75% improvement in forecasting accuracy compared to the traditional ARIMA model.

Keywords
Tehran Stock Exchange (TSE)
Stock Price Forecasting
TOPSIS Ranking Method
ARIMA Model
RARIMA Model
Subjects

Copyright © Mansoureh Naderipour, Hadis Al-Sadat Hosseini, Mohammad-Bagher Jamali

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

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System Engineering and Productivity

Articles in Press, Accepted Manuscript
Available Online from 06 December 2025

  • Receive Date 10 October 2025
  • Revise Date 15 November 2025
  • Accept Date 06 December 2025
  • First Publish Date 06 December 2025
  • Publish Date 06 December 2025