Predicting the Price Index in the Iranian Stock Market with Emphasis on the Monetary Variables: A Machine Learning Approach

Authors
Majid Shafiei
Parviz Rostamzadeh
Mohammad Rastegar
Zahra Dehghan shabani
Shiraz University
Abstract


The stock market, as one of the vital components of the capital market, is an important part of the country's economy that can manage the flow of capital, optimize capital allocation, and thereby contribute to economic growth and development. More accurate prediction of the stock market trend can help investors' decision-making for higher returns by reducing risk. In general, the stock market is constantly changing and many factors influence the trend of this market, so predicting the patterns of movement in the stock exchange requires sufficient information about the past and influencing factors of the market. This article is part of the forecast of the stock market index of Iran, seeking to interpret the model and identify the most influential economic variable on the price index prediction. For this purpose, daily stock market and economic data, during the period 1394-1401 were used. Machine learning models are also used for prediction and the Shapley Additive exPlanations (SHAP) to interpret how to predict and determine the most important variables in the predictive model. Based on results from tree-based ensemble methods, the proposed model in this study, ExtraTrees, performed best based on predictive error criteria. In the study of the feature importance is also based on the ExtraTrees model, in order of the dollar rate (Nima), unemployment rate, dollar rate of market and liquidity, the most important economic variables influencing the forecast model. Also, according to other models used in the research, liquidity is the most effective variable on the stock index trend. Finally, it can be said that the most effective monetary variables on the stock market index in Iran are liquidity and exchange rate variables, so monetary policymakers and stock market investors should be more sensitive to these variables in their decisions.
Keywords
Stock Market Index
Predicting
Monetary Variables
Machine Learning
Feature Importance
Abdou, H. A., Elamer, A. A., Abedin, M. Z., & Ibrahim, B. A. (2024). The impact of oil and global markets on Saudi stock market predictability: A machine learning approach. Energy Economics, 132, 107416.
An, Z., Wu, Y., Hao, F., Chen, Y., & He, X. (2024). A novel hierarchical feature selection with local shuffling and models reweighting for stock price forecasting. Expert Systems with Applications, 123482.
Aras, S. (2021). On improving GARCH volatility forecasts for Bitcoin via a meta-learning approach. Knowledge-Based Systems, 230, 107393.
Basher, S. A., & Sadorsky, P. (2022). Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?. Machine Learning with Applications, 9, 100355.
Bâra, A., & Oprea, S. V. (2024). An ensemble learning method for Bitcoin price prediction based on volatility indicators and trend. Engineering Applications of Artificial Intelligence, 133, 107991.
Bhandari, H. N., Rimal, B., Pokhrel, N. R., Rimal, R., Dahal, K. R., & Khatri, R. K. (2022). Predicting stock market index using LSTM. Machine Learning with Applications, 9, 100320.
Beniwal, M., Singh, A., & Kumar, N. (2024). Forecasting multistep daily stock prices for long-term investment decisions: A study of deep learning models on global indices. Engineering Applications of Artificial Intelligence, 129, 107617.
Brownlee, J. (2021). Ensemble learning algorithms with Python: Make better predictions with bagging, boosting, and stacking. Machine Learning Mastery.
Caparrini, A., Arroyo, J., & Mansilla, J. E. (2024). S&P 500 stock selection using machine learning classifiers: A look into the changing role of factors. Research in International Business and Finance, 70, 102336.
Chauvet, M., & Jiang, C. (2023). Nonlinear relationship between monetary policy and stock returns: Evidence from the US. Global Finance Journal, 55, 100796.
De Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons.
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
Kaymak, Ö. Ö., & Kaymak, Y. (2022). Prediction of crude oil prices in COVID-19 outbreak using real data. Chaos, Solitons & Fractals, 158, 111990.
Hull, J. C. (2021). Machine learning in business: An introduction to the world of data science. (No Title).
Hoang, D., & Wiegratz, K. (2022). Machine learning methods in finance: Recent applications and prospects. European Financial Management.
Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications, 38(5), 5311-5319
Kontonikas, A., & Zekaite, Z. (2018). Monetary policy and stock valuation: structural VAR identification and size effects. Quantitative Finance, 18(5), 837-848.
Leippold, M., Wang, Q., & Zhou, W. (2022). Machine learning in the Chinese stock market. Journal of Financial Economics, 145(2), 64-82.
Long, W., Lu, Z., & Cui, L. (2019). Deep learning-based feature engineering for stock price movement prediction. Knowledge-Based Systems, 164, 163-173.
López, A. C., Arroyo, J., & Mansilla, J. E. (2024). S&P 500 stock selection using machine learning classifiers: A look into the changing role of factors. Research in International Business and Finance, 102336.
Lu, F., Ma, F., & Guo, Q. (2023). Less is more? New evidence from stock market volatility predictability. International Review of Financial Analysis, 89, 102819.
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
Masini, R. P., Medeiros, M. C., & Mendes, E. F. (2023). Machine learning advances for time series forecasting. Journal of economic surveys, 37(1), 76-111.
Ma, D., & Tanizaki, H. (2021). Fat-tailed stochastic volatility model and the stock market returns in China. China Finance Review International, 11(2), 170-184.
Mustaffa, Z., & Sulaiman, M. H. (2023). Stock price predictive analysis: An application of hybrid barnacles mating optimizer with artificial neural network. International Journal of Cognitive Computing in Engineering, 4, 109-117.
Orte, F., Mira, J., Sánchez, M. J., & Solana, P. (2023). A random forest-based model for crypto asset forecasts in futures markets with out-of-sample prediction. Research in International Business and Finance, 64, 101829.
Solayman, S., Aumi, S. A., Mery, C. S., Mubassir, M., & Khan, R. (2023). Automatic COVID-19 prediction using explainable machine learning techniques. International Journal of Cognitive Computing in Engineering, 4, 36-46.
Shen, Z., Fan, X., Zhang, L., & Yu, H. (2022). Wind speed prediction of unmanned sailboat based on CNN and LSTM hybrid neural network. Ocean Engineering, 254, 111352.
Srivastava, T., Mullick, I., & Bedi, J. (2024). Association mining based deep learning approach for financial time-series forecasting. Applied Soft Computing, 111469.
Toochaei, M. R., & Moeini, F. (2023). Evaluating the performance of ensemble classifiers in stock returns prediction using effective features. Expert Systems with Applications, 213, 119186.
Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia computer science, 167, 599-606.
Wang, J.-H., Liao, Y.-L., Tsai, T.-M., & Hung, G. (2006). Technology-based financial frauds in Taiwan: issues and approaches. In 2006 IEEE International Conference on Systems, Man and Cybernetics, 1120 – 1124. IEEE.
Yan, Y., Wang, X., Ren, F., Shao, Z., & Tian, C. (2022). Wind speed prediction using a hybrid model of EEMD and LSTM considering seasonal features. Energy Reports, 8, 8965-8980.
Zhao, C., Yuan, X., Long, J., Jin, L., & Guan, B. (2023). Financial indicators analysis using machine learning: Evidence from Chinese stock market. Finance Research Letters, 58, 104590.
Zhu, R., Zhong, G. Y., & Li, J. C. (2024). Forecasting price in a new hybrid neural network model with machine learning. Expert Systems with Applications, 123697.
Kapurchali, Zomorodian & minooei (2022). Designing a model to explain the effect of effective factors on predicting the profits of companies listed on the Tehran Stock Exchange using machine learning, neural network and linear regression models". Management Accounting and Auditing Knowledge, 13(50), 245-257. [In Persian].
Sharif far, Khalil araghi & Raeisi vanani (2022). Application of deep learning architectures in stock price forecasting (convolutional neural network approach(CNN)). Asset management and financing, 10(3), 1-20. [In Persian].
Torabipur & Siadat (2022). A method for predicting the share price of Tehran stock market based on deep learning. Electronic and cyber defense, 10(4), 91-100. [In Persian].