Predicting the Tehran Stock Exchange Index Using Support Vector Regression; Based on the Dimension Reduction Technique

Document Type : Research Paper

Authors

1 Ph.D. Candidate of Financial Management and Insurance, 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

Abstract

Stock markets play a significant role in organizing modern economic systems. Several research projects have been performed in the field of prediction using intelligent techniques. Considering that the accuracy of these techniques is significantly affected by their input features, one of the improvements made in the use of intelligent models, in addition to the application of hybrid models, is the use of dimensionality reduction as a preprocessing for the prediction model. In this study, in order to predict the daily index of the Tehran Stock Exchange, two methods of dimensional reduction (selection and extraction) are used simultaneously to select appropriate features as model inputs. Hence, the MRMR-MID algorithm is used to select features and the PCA algorithm is used to extract features. Then, support vector regression is used as a prediction model. Finally, an algorithm for selecting suitable features is proposed as ISF_ MID, according to the results obtained from the analysis of the use of dimensional reduction techniques in the prediction model. The results show that with the proposed method, with 7 selected features, it is possible to achieve high accuracy in predicting the daily index of the Tehran Stock Exchange. It should be noted that the studied models were evaluated in the implementation stage by the k-fold cross-validation method. MAE, MSE, and RMSE criteria are also used to evaluate the performance of these models.

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References

Bajalan, S., Fallahpour, S., Dana, N. (2017). “Predicting stock price trends using a modified support vector machine with hybrid feature selection”. Financial Management Perspective, 7(17), 69-86. (in Persian).
Bustos, O. Pomares-Quimbaya, A. (2020). “Stock Market Movement Forecast: A Systematic Review”, Expert Systems with Applications, Volume 156,15 October,113464.
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.
Ding, C. and H. Peng (2005). “Minimum redundancy feature selection from microarray gene expression data”. Journal of bioinformatics and computational biology. 3(2), 185-205.
Guo-Qiang, X. (2011). “The optimization of share price prediction model based on 1712 support vector machine”. In International conference on control, automation and 1713 systems engineering (pp. 1–4). IEEE.
Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). “Literature review: Machine learning techniques applied to financial market prediction”. Expert Systems with Applications. Volume 124, 15 June. 226-251.
Huang, C.-F. (2012). “A hybrid stock selection model using genetic algorithms and support vector regression”. Applied Soft Computing, 12 (2), 807–818.
Kara, Y. , Boyacioglu, M. A. , & Baykan, O. 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.
Kumar, Deepak. Sarangi, Pradeepta Kumar & Verma, Rajit. (2021). “A systematic review of stock market prediction using machine learning and statistical techniques”, Materials Today: Proceedings.
Lee, Ming.Chi (2009). “Using support vector machine with a hybrid feature selection method to the stock trend prediction”. Expert Systems with Applications.Volume 36. Issue 8, 10896-10904.
Lui, Y., and Zheng, Y.F. (2006). “FS_SFS: A novel feature selection method for support vector machines”. Pattern Recognition. Volume 39, Issue 7, July 2006, Pages 1333-1345.
Mandal. M and Mukhopadhyay. A. (2013). “An improved minimum redundancy maximum relevance approach for feature selection in gene expression data”. Procedia Technol.10, 20–27.  
Mansourfar, Gholamreza. Ghayour, Farzad, Khaleghparast Athari, Shabnam. (2015). “Predicting the Industry Index Volatility of Companies Listed in Tehran Stock Exchange, Emphasizing on Corporate Financial Variables Using Support Vector Machine”. Journal of Empirical Studies in Financial Accounting, Volume:12 Issue: 46. (in Persian).
Monajemi, Amirhassan Ebrazi, Medi & Rayati, Alireza. (2009). “Stock price prediction in Tehran stock exchange using artificial neural network”. Journal of financial economy, 6(3), 1-26. (in Persian).
Nevasalmi, Lauri. (2020). “Forecasting multinomial stock returns using machine learning methods”. The Journal of Finance and Data Science, Volume 6, 86-106.
Nguyen, Duc-Hien, Le Manh-Thanh. (2014). “A two-stage architecture for stock price forecasting by combining SOM and fuzzy-SVM”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 12, No. 8, August.
Ni, L.P., Ni, Zh. W., & Gao, Y.Zh. (2011). Stock trend prediction based on fractal feature selection and support vector machine. Expert Systems with Applications, 38(5): 5569-5576.
Ou, P., & Wang, H. (2009). “Prediction of stock market index movement by ten data mining techniques”. Modern Applied Science, 3, P28.
Patel, J., Shah, S., Thakkar, P., and Kotecha, K. (2015). “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques”. Expert Systems with Applications, 42(1):259–268.
Pearson, K. (1901). “On lines and planes of closest fit to systems of points in space”. Philosophical Magazine, 2(6), 559–572.
Perez-Rodriguez, J. V., S. Torrab and J. Andrada-Felixa (2004). “STAR and ANN models: Forecasting performance on the Spanish Ibex-35 stock index”. Journal of Empirical Finance.12(3), 490–509.
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(4), 1-23. (in Persian).
Rafiuzzaman, M. (2014). “Forecasting Chaotic Stock Market Data using Time Series Data Mining”. International Journal of Computer Applications. 101(10), 27–34.
Singh, R. and Srivastava, S. (2017). “Stock prediction using deep learning”. Multimedia Tools and Applications, 76(18):18569–18584.
Ul Haq, Anwar. Zeb, Adnan. Lei, Zhenfeng & Zhang, Defu. (2021). “Forecasting daily stock trend using multi-filter feature selection and deep learning”, Expert Systems with Applications, 168 (2021) 114444
Wanga, Diya & Zhao, Yixi (2020). “Using News to Predicton Investor Sentiment: Based on SVM Model”, Procedia Computer Science.  Wolume 174 .191–199
Wei, Z. (2012). A svm approach in forecasting the moving direction Chinese stock indices, Department of industrial and systems engineering, Thesis of Master of Sciences, Lehigh University.
Yuan, Y. (2013). “Forecasting the movement direction of exchange rate with polynomial smooth support vector machine”. Mathematical and Computer Modelling, 57 (3), 932–944.
Zhang, X., Hu, Y., Xie, K., Wang, S., Ngai, E. W. T., & Liu, M. (2014). “A causal feature selection algorithm for stock prediction modeling”. Neurocomputing, 142. 48-59.
Zhong, X., & Enke, D. (2017). “Forecasting daily stock market return using dimensionality reduction”. Expert Systems with Applications, 67, 126–13.

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History

  • Receive Date: 30 March 2021
  • Revise Date: 13 July 2021
  • Accept Date: 10 September 2022

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