Development of a Comprehensive Model for Predicting Stock Prices in the Stock Market Using an Interpretive Structural Modeling Approach

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.

2 Associate Prof., Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.

3 Assistant Prof., Department of Accounting, Faculty of Accounting, Nowshahr Branch, Islamic Azad University, Nowshahr, Iran.

10.22059/frj.2023.364348.1007501

Abstract

Objective
This study aims to create a comprehensive stock price prediction model for the Tehran Stock Exchange by employing both fuzzy Delphi and interpretive structural modeling techniques. The research is innovative in that it incorporates all relevant factors from technical, fundamental, macroeconomic, and sentiment perspectives for stock price forecasting. This integration is facilitated through mathematical hierarchical modeling to pinpoint the most significant and responsive criteria for predicting stock prices.
 
Methods
Initially, due to potential uncertainties in expert responses, the fuzzy Delphi method was employed. Data analysis identified 54 stock price prediction criteria extracted from the literature. These were presented in two stages to experts and investors in the Tehran Stock Exchange industry as well as university professors. Accordingly, 15 criteria were selected as the most significant and influential, including five technical indicators: exponential moving average, price channel, relative strength, on-balance volume, and price. Additionally, the exchange rate from the macroeconomic component; trading volume from the behavioral component; price-to-earnings ratio, operating profit margin, gross profit margin, sales growth rate, dividend per share, earnings per share, and purchase per share from the fundamental component were chosen. Subsequently, using interpretive structural modeling, the relationships among them were examined and a hierarchical model was established. Interpretive structural modeling aids in determining the sequence and purpose of complex interrelationships among elements within a system.
 
Results
The findings from the interpretive structural modeling revealed that the price-to-earnings per share ratio and the money flow index are positioned at the bottom of the hierarchy, indicating they possess a high driving force in influencing stock prices. Because the product pricing methods of organizations have a significant impact on purchases by investors, the expansionary or contractionary monetary policy plays a crucial role in pricing. The criteria at the bottom of the hierarchy include the exchange rate, a key macroeconomic factor, along with the relative strength indicator and the exponential moving average, both of which are considered significant technical indicators. The findings of this research provide organizations, investors, and active industries in the stock market with a hierarchical model of the most significant factors influencing stock prices in the Tehran Stock Exchange.
 
Conclusion
The results from interpretive structural modeling show that macroeconomic variables such as inflation rate, liquidity growth rate, and exchange rate can significantly influence stock prices. This influence occurs because individuals maintain diverse portfolios of cash, stocks, bank deposits, participatory bonds, gold, and foreign currency. Additionally, these variables impact the financial health of economic enterprises, which in turn affects their stock values.

Keywords

Main Subjects

References

 
Ahmedpour, A., Akbarpour Shirazi, M. & Razavi Amiri, Z. (2009). Using multi-indicator decision making models in stock selection (pharmaceutical company admitted to Tehran Stock Exchange). Tehran Stock Exchange Quarterly, 2(5), 5-38. (in Persian)
Abad, C., Thore, S. A., Laffarga, J. (2004). Fundamental analysis of stocks by two-stage DEA. Managerial and Decision Economics, 25(5), 231-241.
Agrawal, M., Shukla, P. K., Nair, R., Nayyar, A. & Masud, M. (2022). Stock Prediction Based OnTechnical Indicators Using Deep Learning Model. Computers, Materials & Continua., 70(1), 287- 304.
Ang, A. & Bekaert, G. (2007). Return predictability: Is it there? Review of Financial Studies, 20(3), 651–707.
Arshinova, T. (2011). Construction of equity portfolio on the basis of data envelopment analysis approach. Scientific Journal of Riga Technical University. Computer Sciences, 45(1), 104–108.
Bettman, J. L., Sault, S. J., Schultz, E. L. (2009). Fundamental and technical analysis: Substitutes or complements? Accounting & Finance, 49(1), 21–36.
Brock, W., Lakonishok, J. & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns, Journal of Finance, 47, 1731–1764.
Campbell, J. Y. (1987). Stock returns and the term structure. Journal of Financial Economics, 18(2), 373–399.
Campbell, J. Y. & Shiller, R. J. (1988). Stock prices, earnings, and expected dividends, Journal of Finance, 43, 661–676.
Campbell, J. Y. (2002). Strategic Asset Allocation: Portfolio Choice for Long-Term Investors. Oxford University Press.
Campbell, J. Y., & Yogo, M. (2006). Efficient tests of stock return predictability, Journal of Financial Economics, 81(1), 27–60.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P. & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.
Chen, H. H. (2008). Stock selection using data envelopment analysis. Management & Data Systems, 108(9), 1255-1268.
Damodaran, A. (2007). Stock valuation, concepts and applications, compiled by Amin Capital Funding Company (1th ed.). Tehran: Fara Publishing House. (in Persian)
Dase R.K. & Pawar, D. D. (2010). Application of Artificial Neural Network for stock market predictions: A review of literature. International Journal of Machine Intelligence 2(2), 14-17.
Dia, M. (2009). A portfolio selection methodology based on data envelopment analysis. Information Systems and Operational Research, 47(1), 71–79.
Ding, G. & Qin, L. (2019). Study on the prediction of stock price based on the associated network model of LSTM. International Journal of Machine Learning and Cybernetics, 11(6), 1307-1317. https://doi.org/org/10.1007/s13042-019-01041-1
Ejaz, S., Amir, H. & Shabbir, M. S. (2017). Public expenditure and its impact on economic growth: A case of Pakistan. Kashmir Economic Review, 26(1), 13–21.
Fama, E. F. & French, K.R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F. & Schwert, G.W. (1977). Asset returns and inflation, Journal of Financial Economics, 5(2), 115-146.
Gardijan, M. & Kojić, V. (2012). DEA-based investment strategy and its application in the Croatian stock market. Croatian Operational Research Review, 3(1), 203–212.
Garkaz, M. & Pesarakloo, F. (2011). Determination of portfolio through fuzzy data envelopment analysis in companies accepted in Tehran securities exchange. Middle East Journal of Scientific Research, 8(5), 942–946.
Goh, J., Jiang, F., Tu, J. & Zhou, G. (2013). Forecasting government bond risk premia using technical indicators. In 25th Australasian Finance and Banking Conference.
Hwang, S. N., Lin, C. T., Chuang, W. C. (2007). Stock selection using data envelopment analysis-discriminant analysis. Journal of Information and Optimization Sciences, 28(1), 33–50.
Jiang, M., Liu, J., Zhang, L. & Liu, C. (2020). An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms. Physica A: Statistical Mechanics and its Applications, 541, 122272. https://doi.org/https://doi.org/10.1016/j.physa.2019.122272.
Jiang, W. (2021). Applications of Deep Learning in Stock Market Prediction: Recent Progress. Expert Systems with Applications, 184(115537.).
Lamont, O. (1988). Earnings and expected returns. Journal of Finance, 53(5), 1563–1587.
Lim, S., Oh, K. W. & Zhu, J. (2014). Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market. European Journal of Operational Research, 236(1), 361-368.
Ling, O. P. & Kamil, A. A. (2010). Data envelopment analysis for stocks selection on Bursa Malaysia. Archives of Applied Science Research, 2(5), 11–35.
Liu, J., Fang, S.C. & Chen, H. (2020). Multiplicative data envelopment analysis crossefficiency and stochastic weight space acceptability analysis for group decision making with interval multiplicative preference relations. Information Sciences, 514, 319- 333.
Lo, A. W., Mamaysky, H. & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. Journal of Finance, 55, 1705–1770.
Lopes, A., Lanzer, E., Lima, M. & da Costa Jr, N. (2008). DEA Investment Strategy in the Brazilian Stock Market. Economics Bulletin, 13(2), 1-10.
Manjami, A., Abzari, M. & Raiti Shawazi, A. (2008). Forecasting stock prices in the stock exchange market using fuzzy neural network and genetic algorithm and comparing it with artificial neural network. Ekhozati Qatari Quarterly, 6(3), 1-26. (in Persian)
Mehrara, M., Moeini, A., Ahrari, M. & Hamouni, A. (2008). Modeling and forecasting the stock market index and determining the variables affecting it. Economic Research and Policy Quarterly, 17(50), 31-51. (in Persian)
Moghaddam, B. A., Haleh, H. & Ebrahimijam, S. (2011). Forecasting trend and stock price with adaptive extended kalman filter data fusion. Proceedings of IEEE International Conference on Economics and Finance Research, 119–123.
Muhammad, T., Aziz, T. & Shafiul Alam, M. (2023). Utilizing technical data to discover similar companies in Dhaka stock exchange. A Preprint, 1-7.
Neely, C. J., Rapach, D.E., Tu, J. & Zhou, G. (2014). Forecasting the equity risk premium: the role of technical indicators, Management Science, 60, 1772–1791.
Nelson, C. R. (1976). Inflation and the rates of return on common stock. Journal of Finance, 31, 471–483.
Nguyen, V. K., Shabbir, M. S., Sail, M. S., Thuy, T. H. (2020). Does informal economy impede economic growth? Evidence from an emerging economy. Journal of Sustainable Finance & Investment. https://doi.org/ http://doi.org/10.1080/20430795.2020.1711501
Pan, L. & Mishra, V. (2018). Stock market development and economic growth: Empirical evidence from China. Economic Modelling, 68, 661-673.
Powers, J. & McMullen, P. (2000). Using data envelopment analysis to select efficient large market cap securities. Journal of Business and Management, 7(2), 31-42.
Saleem, H., Shabbir, M. S., & Bilal khan, M. (2020). The short-run and long-run dynamics among FDI, trade openness and economic growth: using a bootstrap ARDL test for co-integration in selected South Asian countries. South Asian Journal of Business Studies, 9(2), 279-295.
Shabbir, M. S. (2016). Contributing factors of inland investment. Global Journal of Management and Business Research, 16(5).
Shabbir, M. S. & Muhammad, I. (2019). The dynamic impact of foreign portfolio investment on stock prices in Pakistan. Transnational Corporations Review, 11(2), 166-178.
Siew, L. W., Fai, L. K., Hoe, L. W. (2017). An empirical investigation on the efficiency of the financial companies in Malaysia with DEA model. American Journal of Information Science and Computer Engineering, 3(3), 32–38.
Sonar, H., Gunasekaran, A., Agrawal, S. & Roy, M. (2022). Role of lean, agile, resilient, green, and sustainable paradigm in supplier selection. Cleaner Logistics and Supply Chain, 4, 100059.
Tehrani, R., Mehragan, M. R., Golkani, M. R. (2012). A model for evaluating financial performance of companies by data envelopment analysis: A case study of 36 corporations affiliated with a private organization. International Business Research, 8(8).
Thakkar, K. C. & Chaudhari, K. (2021). Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions. Information Fusion 65, 95–107.
Thakkar, K. C. & Chaudhari, K. (2020). Cross-reference to exchange-based stock trend prediction using long short-term memory. Procedia Computer science, 167 616–625.
Toloui, A. & Haq Dost, Sh. (2008). Modelling of stock price forecasting using neural network and its comparison with mathematical forecasting methods. Scientific-research quarterly of Economic Research Journal, 7(25), 237-251. (in Persian)
Weng, B., Ahmed, M. A. & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153-163.
Financial Research Journal
Volume 26, Issue 3
2024
Pages 553-578

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