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

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

  • Tehran Stock Exchange. (2020). Tehran Stock Exchange Statistics.; Available from: https://www.tse.ir/en/home.html.
  • Moghaddam, A.H., M.H. Moghaddam, and M. Esfandyari, Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 2016. 21(41): p. 89-93.
  • Ozdemir, O., A. Aslanargun, and S. Asma, ANN forecasting models for ISE national-100 index. Journal of Modern Applied Statistical Methods, 2010. 9: p. 579-583.
  • Ayala, J., et al., technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems, 2021. 225: p. 107119.
  • Pai, P.-F. and C.-S. Lin, A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 2005. 33(6): p. 497-505.
  • Wei, L.-Y., A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX. Economic Modelling, 2013. 33: p. 893-899.
  • Arash Salehpour, E.S., A Regression Analysis on the Car Index in the Tehran Stock Exchange. Journal of Soft Computing Paradigm, 2022. 4(4): p. 238-251.
  • Ahmadi, E., et al., New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic. Expert Systems with Applications, 2018. 94: p. 21-31.
  • Kanwal, A., et al., BiCuDNNLSTM-1dCNN — A hybrid deep learning-based predictive model for stock price prediction. Expert Systems with Applications, 2022. 202: p. 117123.
  • Wu, J.M.-T., et al., A Tool based on ML-driven Graphical Model for Stock Price Prediction by Leading Indicators. IFAC-PapersOnLine, 2020. 53(5): p. 692-697.
  • Abu-Mostafa, Y.S. and A.F. Atiya, Introduction to financial forecasting. Applied Intelligence, 1996. 6(3): p. 205-213.
  • Blank, S.C., " Chaos" in futures markets? A nonlinear dynamical analysis. The Journal of Futures Markets (1986-1998), 1991. 11(6): p. 711.
  • Huang, W., Y. Nakamori, and S.-Y. Wang, Forecasting stock market movement direction with support vector machine. Computers & operations research, 2005. 32(10): p. 2513-2522.
  • Moody, J. and M. Saffell, learning to trade via direct reinforcement. IEEE transactions on neural Networks, 2001. 12(4): p. 875-889.
  • Hsu, M.-W., et al., Bridging the divide in financial market forecasting: machine learners vs. financial economists. Expert systems with Applications, 2016. 61: p. 215-234.
  • Long, W., Z. Lu, and L. Cui, Deep learning-based feature engineering for stock price movement prediction. Knowledge-Based Systems, 2019. 164: p. 163-173.
  • Hochreiter, S. and J.r. Schmidhuber, long short-term memory. Neural computation, 1997. 9(8): p. 1735-1780.
  • Shahrestani, P. and M. Rafei, the impact of oil price shocks on Tehran Stock Exchange returns: Application of the Markov switching vector autoregressive models. Resources Policy, 2020. 65: p. 101579.
  • Ebrahimi, B. and E. Hajizadeh, A novel DEA model for solving performance measurement problems with flexible measures: An application to Tehran Stock Exchange. Measurement, 2021. 179: p. 109444.
  • Ramezanian, R., A. Peymanfar, and S.B. Ebrahimi, An integrated framework of genetic network programming and multi-layer perceptron neural network for prediction of daily stock return: An application in Tehran stock exchange market. Applied Soft Computing, 2019. 82: p. 105551.
  • Chizari, M.h., et al., The impact of Intellectual Capitals of Pharmaceutical Companies Listed in Tehran Stock Exchange on their Market Performance. Procedia Economics and Finance, 2016. 36: p. 291-300.
  • Nejad, M.K., F. Jahantigh, and H. Rahbari, The Long Run Relationship between Oil Price Risk and Tehran Stock Exchange Returns in Presence of Structural Breaks. Procedia Economics and Finance, 2016. 36: p. 201-209.
  • Abounoori, E., Z. Elmi, and Y. Nademi, Forecasting Tehran stock exchange volatility; Markov switching GARCH approach. Physica A: Statistical Mechanics and its Applications, 2016. 445: p. 264-282.
  • Jalilvand, A., M.R. Noroozabad, and J. Switzer, Informed and uninformed investors in Iran: Evidence from the Tehran Stock Exchange. Journal of Economics and Business, 2018. 95: p. 47-58.
  • Mothlagh, S.S., F. Samadi, and Z. Hajiha, The Relationship of the Content of the Market Value in the Explanation of Abnormal Stock Returns of Listed Companies in Tehran Stock Exchange. Procedia Economics and Finance, 2016. 36: p. 113-122.
  • Ebrahimpour, R., et al., Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange. International Journal of Forecasting, 2011. 27(3): p. 804-816.
  • Jahan-Parvar, M.R. and H. Mohammadi, Risk and return in the Tehran stock exchange. The Quarterly Review of Economics and Finance, 2013. 53(3): p. 238-256.
  • Fernando, J. Moving Average (MA): Purpose, Uses, Formula, and Examples. 2022; Available from: https://www.investopedia.com/terms/m/movingaverage.asp.
  • Pattewar, T. and D. Jain. Impact of Covid'19 on Indian Stock Prediction by Technical Indicators. in 2022 International Conference for Advancement in Technology (ICONAT). 2022.
  • Gamboa Valero, H., Big Data finance: trading strategy creation using Deep Reinforcement Learning in University of Manchester 2021 University of Manchester University of Manchester
  • Patel, J., et al., Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 2015. 42(4): p. 2162-2172.
  • Neter, J., et al., Applied linear statistical models. 1996.
  • Seal, H.L., Studies in the History of Probability and Statistics. XV The historical development of the Gauss linear model. Biometrika, 1967. 54(1-2): p. 1-24.
  • Smola, A.J. and B. Schölkopf, A tutorial on support vector regression. Statistics and Computing, 2004. 14(3): p. 199-222.
  • Ho, T.K. Random decision forests. in Proceedings of 3rd international conference on document analysis and recognition. 1995. IEEE.
  • Breiman, L., Random forests. Machine learning, 2001. 45(1): p. 5-32.
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