Predicting Automobile Stock Prices Index in the Tehran Stock Exchange Using Machine Learning Models

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Arash Salehpour 1,*

1. Department of Computer Engineering, Islamic Azad University, Rasht Branch Iran

* Corresponding author.


Received: 10 Apr. 2023 / Revised: 11 Jul. 2023 / Accepted: 24 Aug. 2023 / Published: 8 Oct. 2023

Index Terms

Machine Learning, Stock Price Prediction, Linear Regression, Support Vector Regression, Random Forest, Tehran Stock Exchange


This paper analyses the performance of machine learning models in forecasting the Tehran Stock Exchange's automobile index. Historical daily data from 2018-2022 was pre-processed and used to train Linear Regression (LR), Support Vector Regression (SVR), and Random Forest (RF) models. The models were evaluated on mean absolute error, mean squared error, root mean squared error and R2 score metrics. The results indicate that LR and SVR outperformed RF in predicting automobile stock prices, with LR achieving the lowest error scores. This demonstrates the capability of machine learning techniques to model complex, nonlinear relationships in financial time series data. This pioneering study on a previously unexplored dataset provides empirical evidence that LR and SVR can reliably forecast automobile stock market prices, holding promise for investing applications.

Cite This Paper

Arash Salehpour, "Predicting Automobile Stock Prices Index in the Tehran Stock Exchange Using Machine Learning Models", International Journal of Intelligent Systems and Applications(IJISA), Vol.15, No.5, pp.12-27, 2023. DOI:10.5815/ijisa.2023.05.02


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