Stock Return Forecasting Using the Bayesian Model Approach in Tehran Securities Exchange

Document Type : Research Paper

Authors

Department of Financial Management, Tehran North Branch, Islamic Azad University, Tehran, Iran.

Abstract

In the present research, stock returns were predicted using the Bayesian model approach in Tehran Securities Exchange. Therefore, the research hypothesis that based on Bayesian method has higher accuracy in predicting returns than autoregressive models was developed and tested. In order to examine the hypothesis, information related to the index of 30 selected industries in the Tehran Stock Exchange during the period from 2017/03/25 to 2020/08/24 was used. The index return was predicted based on two methods for 30 out-of-sample data. First autoregressive models were fitted on returns of each index and then the next 30 days of returns were predicted based on these models. Then after identifying the optimal model lags through the Bayesian Model Averaging method, autoregressive models were fitted with the optimal lags and the next 30 days predictions were obtained under this method.
In order to compare the accuracy of the methods in predicting the return, RMSE and MAE criteria were used, and the values of these error criteria were compared using Wilcoxon Nonparametric Pairwise comparison tests.
The results showed that Bayesian method leads to increase the accuracy of model prediction in out of sample data.

Keywords

References

       [1]        Hadi Doulabi, N., M. Rastegar, P. Mohammadi, Measuring the Stock Liquidity Using a Market Microstructure Approach. Advances in Industrial Engineering, 2020. 54(3), 311-331. doi: 10.22059/jieng.2021.325016.1770
       [2]        Kumar, G., and A. K. Misra, Closer view at the stock market liquidity: A literature review. Asian Journal of Finance and Accounting, 2015. 7(2), 35-57.
       [3]        Alamatian, Z., M. Vafaei Jahan, Iran Stock Market Prediction Based on Bayesian Networks and Hidden Markov Models. 2017. 8(33), 283-298.
       [4]        Quah, H., J. Haman, and D. Naidu, The effect of stock liquidity on investment efficiency under financing constraints and asymmetric information: Evidence from the United States. Accounting and Finance, 2021. 61, 2109-2150
       [5]        Będowska-Sójka, B., Commonality in liquidity measures. The evidence from the Polish stock market. 2019.
       [6]        Johann, T., S. Scharnowski, E. Theissen, C. Westheide, and L. Zimmermann, Liquidity in the German stock market. Schmalenbach Business Review, 2019. 71(4), 443-473
       [7]        Fakhari, H., M. Valipour Khatir, M. Mousavi, Investigating Performance of Bayesian and Levenberg-Marquardt Neural Network in Comparison Classical Models in Stock Price Forecasting. Financial Research Journal, 2017. 19(2), 299-318. doi: 10.22059/jfr.2017.214203.1006264
       [8]        Sousa J. M.,R. M. Sousa, Asset Returns Under Model Uncertainty: Evidence from the Euro Area, the US and the UK, Comput Econ, 2017. DOI 10.1007/s10614-017-9696-2
       [9]        Rather, A. M., A. Agarwal, V. N. Sastry, Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 2015. 42(6), 3234-3241
     [10]      Susannah H. M., C. Curme, A. Avakian, Y. Dror, H. Kenett, E. Stanley, T. Preis, Quantifying Wikipedia Usage Patterns Before Stock Market Moves. Scientific Reports, 2013. 3: 1801.
     [11]      Wang H., R. Chatpatanasiri, P. Sattayatham, Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental, Investment School of Mathematics, Suranaree University of Technology, 2017. THAILAND.
     [12]      Zhou, X., J. Wang, X. Yang, B. Lev, Y. Tu, S. Wang, Portfolio selection under different attitudes in fuzzy environment. Information Sciences, 2018. 462, 278-289
     [13]      Bollen, J., M. Huina, X. Zeng, Twitter mood predicts the stock market. Cornell University, 2010. Retrieved November 7.
     [14]      Zhang, Y., D. W. Gong, X. Y. Sun, Y. N. Guo,  A PSO-based multi-objective multi-label feature selection method in classification. Scientific reports, 2017. 7(1), 1-12.
     [15]      Schroeder, P., I. Kacem, G. Schmidt, Optimal online algorithms for the portfolio selection problem, bi-directional trading and-search with interrelated prices. RAIROOperations Research, 2019. 53(2), 559-576
     [16]      Mokhtarzadeh, M., R. Tavakkoli-Moghaddam, C. Triki, Y. Rahimi, A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities. Engineering Applications of Artificial Intelligence, 2021. 98, 104121.
     [17]      Malkiel, Burton G.,  A Random Walk Down Wall Street (6th ed.). W.W. Norton & Company, Inc.  1973.
     [18]      Ball P., Counting Google searches predicts market movements. Nature. 2013. doi:10.1038/nature.2013.12879
     [19]      Li, X., Y. H. Huang, S. C. Fang, Y. Zhang,  An alternative efficient representation for the project portfolio selection problem. European Journal of Operational Research, 2020. 281(1), 100-113.
     [20]      Beckmann, M.,  Doctoral Thesis: Stock Price Change Prediction Using News Text Mining. COPPE/Federal University of Rio de Janeiro, 2017.
     [21]      Landsman, Z., U. Makov, T. Shushi, A generalized measure for the optimal portfolio selection problem and its explicit solution. Risks, 2018. 6(1), 19.
     [22]      Garcia, F., J. González-Bueno, J. Oliver, R. Tamošiūnienė, A credibilistic meansemivariance-PER portfolio selection model for Latin America. Journal of Business Economics and Management, 2019. 20(2), 225-243.
     [23]      Jacaruso, L. C.,  A method of trend forecasting for financial and geopolitical data: inferring the effects of unknown exogenous variables. Journal of Big Data, 2018. 5 (1): 47.
     [24]      Sobhanifard, F., M. Shahraki, An Integrated Neural Networks and MCMC Model to Predicting Bank’s Efficiency. Advances in Industrial Engineering, 2020. 54(1), 1-14. doi: 10.22059/jieng.2021.312818.1743
     [25]      Wanke, P., M. D. A. Azad, C. P. Barros,  Predicting effciency in Malaysian Islamic bank: A two-stage TOPSIS and neural networks approach. Business and Finance, 2016. 36, 485–498.
     [26]      Zhang, Y., L. Wu, Stock Market Prediction of S&P 500 via combination of improved BCO Approach and BP Neural Network. Expert Systems with Applications, 2009.36 (5): 8849–8854.
     [27]      Atashgar, K., N. Rafiee, Identification of the change point in panel data using simultaneously EWMAA and CUSUM. Advances in Industrial Engineering, 2019. 53(1), 471-481.
     [28]      Harvey, A., P. Kattuman, Time series models based on growth curves with applications to forecasting coronavirus. Harvard Data Science Review, 2020.
     [29]      Kambouroudis, D. S., D. G. McMillan, K. Tsakou, Forecasting stock return volatility: a comparison of GARCH, Implied volatility, and realized volatility models. Journal of futures markets, 2016. 36(12), 1127-1163.
     [30]      Cho, H., Change-Point Detection in Panel Data Via Double CUSUM Statistic, Electronic Journal of Statistics, 2016. 10(2), 2000-2038.
     [31]      Enomoto, T., Y. Nagata, Detection of Change Points in Panel Data Based on Bayesian MT Method, Total Quality Science, 2016. 2(1), 36-47.
     [32]      Chen S., Predicting Stock Returns Using Firm Characteristics: A Bayesian Model Averaging Approach, Department of Economics and Finance, City University of Hong Kong, 2018.
     [33]      Gao Z., Stock Investment Selection Management Based on Bayesian Method, Advances in Economics, Business and Management Research, 2018. Vol. 75, 407-413.
     [34]      Atkins A., M. Niranjan, E. Gerding, Financial news predicts stock market volatility better than close price, The Journal of Finance and Data Science, 2018. 4(2), 120-137.
     [35]      Hamidian M., S. Boostani , H. Mashhadi, Predicting negative returns on stocks of companies listed on the Iranian capital market, Journal of Decisions and Operations Research, 2019. 4(2), 30-40. (In Persian)
     [36]      Barzegari Khanagha, J., Z. Jamali, Predicting Stock Returns with Financial Ratios; An Exploration in Recent Researches. Journal of accounting and social interests, 2016. 6(2), 71-92. (In Persian)
     [37]      Salehirad M. R., N. Habibifard, Comparing of Bayesian Model Selection Based on MCMC Method and Finance Time Series. Financial knowledge of security analysis, 2012. 5(15), 59-67. (In Persian)
     [38]      Dehghan Dehnavi, M., M. Bahrololoum,, M. Peymany Foroushany, S. Raeiszadeh, Portfolio Selection Optimization Problem Under Systemic Risks. Advances in Industrial Engineering, 2020. 54(2), 121-140.
Advances in Industrial Engineering
Volume 55, Issue 3
July 2021
Pages 233-247

Files

Share

How to cite

Statistics