Feature Selection for the Prediction Model of the Tehran Stock Exchange Index by Dimensionality Reduction Techniques

Document Type : Research Paper


1 Ph.D. Candidate, Department of Financial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.

2 Associate Prof., Department of Financial Management and Insurance, Faculty of Management & Accounting, Shahid Beheshti University, Tehran, Iran.

3 Assistant Prof., Department of Financial Management and Insurance, Faculty of Management & Accounting, Shahid Beheshti University, Tehran, Iran.

4 Prof., Department of Business Management, Faculty of Management & Accounting, Shahid Beheshti University, Tehran, Iran.



Objective: The main purpose of this study is to select an appropriate model for daily prediction of the total index of the Tehran Stock Exchange (TEDPIX). In this regard, dimension reduction techniques have been used to select effective and representative features to increase the accuracy of the selected model.
Methods: Since dimensionality reduction can be performed by two different methods (feature selection and extraction), in this study, two methods were used simultaneously to select the appropriate features of the prediction model. Hence, the MID algorithm was used to select the features, and the PCA algorithm was used to extract them. In this regard, after collecting 34 financial and economic features affecting the stock market, the features were prioritized by the MID algorithm. Then, the appropriate model was selected by comparing the performance of two different neural network models called RBF and DNN, which are respectively the most important and innovative of the extant models. Then, using two types of dimensionality reduction techniques, the prediction accuracy of the selected model was examined. The appropriate method for selecting the input features of the prediction model was identified, accordingly.
Results: Analysis of the obtained results showed that the RBF model comes with more accuracy in the daily prediction of the Tehran Exchange Dividend and Price Index. Also, by comparing the performance of the two types of dimensionality reduction techniques, it was found that compared with the PCA algorithm, the MID algorithm brings better results in selecting the input variables of the RBF model. Therefore, according to the priority of features with the MID algorithm and the pattern of changing the level of error by increasing the number of features in the RBF model, the ISF-MID algorithm was proposed to select the appropriate features of the stock index prediction model. Using this algorithm, with the minimum number of features, can end in the highest accuracy in predicting the total index of the Tehran Stock Exchange.
Conclusion: The proposed method can identify, prioritize and select appropriate features for the prediction model, due to the simplicity and effectiveness of its use. It can also be useful in various areas of modeling, including the capital market, foreign exchange market, etc.


Afshari Rad, E., Alavi, S. & Sinaei, H. (2018). Developing an Intelligent Model to Predict Stock Trend Using the Technical Analysis. Financial Research Journal, 20(2), 249-264.
(in Persian)
Asima, M., Ali Abbaszadeh Asl, A. (2019). Developing a Hybrid Model to Estimate Expected Return Based on Genetic Algorithm. Financial Research Journal, 21(1), 101-120.
(in Persian)
Atsalakis, G. S. & Valavanis, K. P. (2009). Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications, 36 (3), 5932–5941.
Bengio, Y., Courville, A. & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (8), 1798-1828.
Bustos, O., Pomares, A. & Gonzalez, E. (2018). A comparison between SVM and multilayer perceptron in predicting an emerging financial market: Colombian stock market. In 2017 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2017- Conference Proceedings, Institute of Electrical and Electronics Engineers Inc, January, 1-6.
Cavalcante, R. C., Brasileiro, R. C., Souza V. L.F., Nobrega, J. P. & Oliveira A. L. I. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions, Expert Systems with Applications, 55: 194-211.
Cervelló-Royo, R. & Guijarro, F. (2020). Forecasting stock market trend: A comparison of machine learning algorithms. Finance, Markets and Valuation, 6(1), 37–49.
Chen, Q., Zhang, W. & Lou, Y. (2020). Forecasting stock prices using a hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network, IEEE Access, 8, 117365-117376.
Cheng, C. H. & Wei, L. Y. (2014). A novel time-series model based on empirical mode decomposition for forecasting TAIEX. Economic Modelling, 36: 136–141.
Chong, E., Han, C. & Park, F. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83: 187–205.
Dash, R. & Dash, P. (2016). A comparative study of radial basis function network with different basis functions for stock trend prediction, In 2015 IEEE Power,Communication and Information Technology Conference, (PCITC 2015), Institute of Electrical and Electronics Engineers Inc. 430-435.
Ding, X., Zhang, Y., Liu, T. & Duan, J. (2015). Deep learning for event-driven stock prediction. In Proceedings of the 24th International Conference on Artificial Intelligence: 2327-2333. AAAI Press.
Ebadati, O. M. & Mortazavi, M. (2016). An efficient hybrid machine learning method for time series stock market forecasting. Neural Network World, 28(1), 41-55.
Faghihi Nezhad, M. & Minaei, B. (2018). Prediction of Stock Market Behavior Based on Artificial Neural Networks through Intelligent Ensemble Learning Approach. Industrial Management Journal, 10(2), 315-334. (in Persian)
Gunduz, H. & Cataltepe, Z. (2015). Borsa Istanbul (BIST) daily prediction using financial news and balanced feature selection. Expert Systems with Applications, 42 (22), 9001-9011.
Gündüz, H., Çataltepe, Z. & Yaslan, Y. (2017). Stock daily return prediction using expanded features and feature selection. Turkish Journal of Electrical Engineering & Computer Sciences, 25(6), 4829-4840.
Guo, Z., Ye, W., Yang, J. & Zeng, Y. (2017). Financial index time series prediction based on bidirectional two dimensional locality preserving projection, In 2017 IEEE 2nd International Conference on Big Data Analysis, Institute of Electrical and Electronics Engineers Inc, 934-938.
Guresen, E., Kayakutlu, G. & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert systems with application, 38, 10389-10397.
Henrique, B. M., Sobreiro, V. A. & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124: 226-251.
Hinton, G. E. & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507.
Hinton, G. E., Osindero, S. & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.
Hu, H., Tang, L., Zhang, S. & Wang, H. (2018). Predicting the direction of stock markets using optimized neural networks with google trends. Neurocomputing, 285, 188-195.
Huang, C. L. & Tsai, C. Y. (2009). A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting. Expert Systems with Applications, 36 (2), 1529-1539.
Kimoto, T., Asakawa, K., Yoda, M. & Takeoka, M. (1990). Stock market prediction system with modular neural network, in International Joint Conference on Neural Networks, 1-6.
Kuremoto, T., Kimura, S., Kobayashi, K. & Obayashi, M. (2014). Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing, 137: 47-56.
Le Roux, N. & Bengio, Y. (2008). Representational power of restricted Boltzmann machines and deep belief networks. Neural Computation, 20 (6), 1631-1649.
Lee, H., Grosse, R., Ranganath, R. & Ng, A. Y. (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th annual international conference on machine learning, Montreal, Canada: 609-616.
Lee, M. C. (2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, 36 (8):10896–10904.
Lin, Y., Guo, H., & Hu, J. (2013). An svm-based approach for stock market trend prediction. In The 2013 international joint conference on neural networks (IJCNN), IEEE. 1-7
Long, W., Lu, Z. & Cui, L. (2019). Deep learning-based feature engineering for stock price movement prediction. Knowledge-Based Systems, 164, 163-173.
Monfared, J., Alinejad, M. & Metghalchi, S. (2012). A Comparative Study of Neural Network Models With Box Jenkins Methodologies in Prediction of Tehran Price Index(TEPIX). Financial Engineering and Securities management (Portfolio Management), 3(11), 1-16. (in Persian)
Nahil, A. & Lyhyaoui, A. (2018). Short-term stock price forecasting using kernel principal component analysis and support vector machines: the case of Casablanca stock exchange. Procedia Computer Science, 127, 161-169.
Nelson, M. & Illingworth, W. (1991). A Practical Guide to Neural Nets, Boston, MA: Addison-Wesley Publishing Company.
Niku sokhan, M. (2018). An Improved Hybrid Model with Automated Lag Selection to Forecast Stock Market. Financial Research Journal, 20(3), 389-408. (in Persian)
Pashootanizadeh, H., Ranaei Kordshouli, H., Abbasi, A. & Moosavi haghighi, M. (2020). Simulation the Model of Effects of Behavioral and Macroeconomic Factors on the Tehran Stock Exchange Index with Using System Dynamics Approach. Journal of Financial Management Perspective, 10(29), 89-124. (in Persian)
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(6), 559-572.
Qiu, M., and Y. Song. (2016). Predicting the Direction of Stock Market Index Movement Using
          an Optimized Artificial Neural Network Model. PLoS ONE, 11(5), 1-11.
Raee, R., Nikahd, A. & Habibi, M. (2017). The Index Prediction of Tehran Stock Exchange by Combining the Principal Components Analysis, Support Vector Regression and Particle Swarm Optimization. Financial Management Strategy, 4(15), 1-23. (in Persian)
Salehi, M. & Garshasbi, F. (2019). Tehran Stock Exchange Index Forecasting Using Approach Adaptive Neural-Fuzzy Inference System and Imperialist Competitive Algorithm. IT Management Studies, 8(29), 5-34. (in Persian
Seif, S., Jamshidi navid, B., Ghanbari, M. & Esmaeil pour, M. (2021). Predicting Stock Market Trends of Iran Using Elliott Wave Oscillation and Relative Strength Index. Financial Research Journal, 23(1), 134-157. (in Persian)
Sezer, O. B., Gudelek, M. U. & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing, 90, 1-63.
Singh, R. & Srivastava, S. (2017). Stock prediction using deep learning. Multimedia Tools and Applications, 76(18), 18569-18584.
Taghavi, R., Dadashi, I., Zare bahnamiri, M. & Gholamnia roshan, H. (2020). Predicting Emotional Tendency of Investors Using Support Vector Machine (SVM) and Decision Tree (DT) Techniques. Financial engineering portfolio securities management, 11(45), 544-570. (in Persian)
Tehrani, R., Heyrani, M. & Mansuri, S. (2019). A Comparison between Fama and French five-factor model and artificial neural networks in predicting the stock price. Financial Engineering and Securities management(Portfolio Management), 10(39), 278-294.
(in Persian
Ul Haq, A., Zeb, A., Lei, Z. & Zhang, D. (2021).  Forecasting daily stock trend using multi-filter feature selection and deep learning. Expert Systems with Applications, 168(3), 1-8.
Yoo, P., Kim, M. & Jan, T. (2005). Machine learning techniques and use of event information for stock market prediction: A survey and evaluation. In Proceedings - International Conference on Computational Intelligence for Modeling, Control and Automation, (CIMCA 2005). Piscataway, NJ: IEEE, 835-841.
Zhang, X. L. & Wu, J. (2013). Deep belief networks based voice activity detection. IEEE Transactions on Audio, Speech, and Language Processing, 21 (4), 697-710.
Zhong, X. & Enke, D. (2017). A comprehensive cluster and classification mining procedure for daily stock market return forecasting. Neurocomputing, 267, 152–168.
Zhong, X. & Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 1-20.
Zolfaghari, M., Sahabi, B. & Bakhtyaran, M. (2020). Designing a Model for Forecasting the Stock Exchange Total Index Returns (Emphasizing on Combined Deep Learning Network Models and GARCH Family Models). Financial Engineering and Securities management(Portfolio Management), 11(42), 138-171. (in Persian)