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

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

Автор: Arash Salehpour

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 5 vol.15, 2023 года.

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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.

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

Короткий адрес: https://sciup.org/15019010

IDR: 15019010   |   DOI: 10.5815/ijisa.2023.05.02

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