P/E Modeling and Prediction of Firms Listed on the Tehran Stock Exchange; a New Approach to Harmony Search Algorithm and Neural Network Hybridization

Document Type : Research Paper

Authors

Department of Accounting, Islamic Azad University, Kashan Branch, Kashan, Iran

Abstract

Investors and other contributors to stock exchange need a variety of tools, measures, and information in order to make decisions. One of the most common tools and criteria of decision makers is price-to earnings per share ratio. As a result, investors are in pursuit of ways to have a better assessment and forecast of price and dividends and get the highest returns on their investment. Previous research shows that neural networks have better predictability than statistical models. Thus, Harmony Search algorithm and neural network have been used in this work, since achieving the best forecast is more likely. For this purpose, a sample consisting of 87 companies has been selected from those listed at the Tehran Stock Exchange over a 10-year period (2006-2015). The results show the high accuracy of the designed model that predicts the price-to-earnings ratio at the stock exchange by hybridizing the balanced search algorithm with neural network.

Keywords

Main Subjects

Article Title [فارسی]

مدلسازی و پیش بینی P/E شرکت‎های پذیرفته شده در بورس اوراق بهادار تهران؛ رویکرد نوین ترکیب الگوریتم هارمونی سرچ و شبکه عصبی

Authors [فارسی]

  • مژگان صفا
  • حسین پناهیان
گروه حسابداری، واحد کاشان، دانشگاه آزاد اسلامی، کاشان ، ایران
Abstract [فارسی]

سرمایه‌گذاران و سایر مشارکت‌کنندگان در بورس اوراق بهادار به‌منظور تصمیم‌گیری نیازمند ابزارها، معیارها و اطلاعات متنوع و متعددی می‌باشند یکی از رایج‌ترین ابزارها و معیارهای تصمیم‌گذران، ضریب قیمت‌به‌سود هر سهم می‌باشد. در نتیجه سرمایه‌گذاران به دنبال روش‌هایی هستند تا ارزیابی و پیش‌بینی بهتری را از قیمت و سود سهام داشته باشند و بالاترین بازدهی را از سرمایه‌گذاری خود کسب نمایند. تحقیقات قبلی نشان می‌دهد که شبکه‌های عصبی نسبت به مدل‌های آماری قابلیت پیش‌بینی بیشتری دارند. در این تحقیق از الگوریتم‌ هارمونی سرچ و شبکه عصبی استفاده شده است که احتمال دست یافتن به بهترین پیش‌بینی افزایش یابد. بدین منظور نمونه‌ای متشکل از 87 شرکت در طی یک‌دوره 10 ساله (1385-1394) از شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران انتخاب شده است. نتایج، نشان‌دهنده دقت بالای مدل‌‌ طراحی شده پیش‌بینی نسبت قیمت‌به‌سود سهام در بورس اوراق بهادار به وسیله ترکیب الگوریتم جستجوی متوازن با شبکه عصبی می‌باشد.

Keywords [فارسی]

  • جستجوی هماهنگ
  • پیش‌بینی نسبت قیمت به سود
  • تحلیل بنیادی
  • اقتصادسنجی داده‌های ترکیبی
  • شبکه عصبی RBF

References

  1. Adebiyi, A. A., Charles, A., Adebiyi, M., and Adebiyi, O. S. (2012, a). Stock Price Prediction using Neural Network with Hybridized Market Indicators. Journal of Emerging Trends in Computing and Information Sciences, 3(1), 1-9.
  2. Adebiyi, A. A., Ayo, C. K., Adebiyi, M. O., and Otokiti, S. O. (2012, b). An Improved Stock Price Prediction using Hybrid Market Indicators. African Journal of Computing & ICT, 5(5), 124-135.
  3. Aflatoni, A. (2015). Investigating determinants to the level of material and goods inventory holdings and their ranking using decision tree and neural network algorithms. Financial Accounting Quarterly, year. 7(27), 52-30. (In Persian)
  4. Amiri, Sh. (2013). A study on dynamic correlation between stock price index, oil price, gold coin and exchange rate in Iran using DCC-GARCH method. Master's Thesis. Mashhad: Faculty of Administrative and Economic Sciences, Ferdowsi University. (In Persian)
  5. Arabi, M. (2005). Comparison of Artificial Neural Network Method with ARIMA Method in Stock Price Forecast; a case study of Petrochemical Industry Investment Company. Master's Thesis, Faculty of the Humanities, Tarbiat Modares University, Tehran. (In Persian)
  6.  Brighton, E., and Selina, E. (2012). Artificial intelligence (first step) (translated by: A. Eskafi). Tehran: Pardis Danesh Publication. (In Persian)
  7. Budhani, N., Jha, C. K., and Budhani, S. K. (2012). Application of Neural Network in Analysis of Stock Market Prediction. International Journal of Computer Science & Engineering Technology (IJCSET), 3(4), 61-68.
  8. DeTienne, K. B.; DeTienne, D. H.; and Joshi, S. A. (2003). “Neural Networks as Statistical Tools for Business Researchers”. Organizational Research Methods, 6(2): 236-265.
  9. Ebadi, A. (2009). Prediction of total stock price index on the Tehran Stock Exchange using artificial neural networks. Master's Degree, Faculty of Economics and Social Sciences, Bu-Ali Sina University, Hamedan. (In Persian)
  10. Eslamlouian, K and Zare, E. (2006). Investigating the effect of macro variables and alternative assets on stock prices in Iran: a self-dependent model with distributive interruptions. Journal of Iran Economic Studies, 8(29), 17-46. (In Persian)
  11. Gao.X. Z, Govindasamy. V, Xu.H, Wang .X, Zenger.K. (2015). Harmony Search Method: Theory and Applications. Hindawi Publishing Corporation Computational Intelligence and Neuroscience. Article ID 258491, 10 pages
  12. Jalili, F, Malkjaafariyan, M. Safavinejad, E. (2013). An introduction of Harmonic Search algorithm to optimization of aerodynamic forms using Navier-Stokes Equations. Journal of Applied and Computational Sciences in Mechanics, 2, 96-81. (In Persian)
  13. Jandaghi, Gh., Tehrani, R.., Hosseinpour, D., Gholipour, R., and Shahidi Shadkam, S. A. (2010). Application of Fuzzy-neural networks in multi-ahead forecast of stock price. African Journal of Business Managemen, 4(6), 903-914.
  14. Janeski, M., and Kalajdziski, s. (2010). Forecasting stock market prices. ICT Innovations, Web proceedings, ISSN 1857-7288, 107-116.
  15. Jolliffe, I. T. (2002). Principal component Analysis. Springer Series in Statistics (2nd ed.). Springer: NewYork.
  16. Karimzadeh, M. (2006). Investigating the long-term relationship between stock price index and cacro-monetary variables using co-integration method in Iranian economy. Journal of Economic Research, 8(26), 41-42. (In Persian)
  17. Lee, K. S., Geem Z.W., Lee S. H., Bae K. W. (2005). The harmony search heuristic algorithm for discrete structural optimization, Eng. Optim. 37(7), 663–684.
  18. Lendass, A., Bodt, D. E., Wertz, V., and Verleysen, M. (2000). Non-Liner financial time series forecasting application to Bell 20 stock market index. European Journal of Economic and Social System, 14(1), 81-91.
  19. Mashayekh, Sh. Khamisi, H. and Farshi, Z. (2013). Investigating the predictability of PEG ratio vis-a-vis P/E ratio to determine stock prices in companies listed on the Tehran Stock Exchange. Journal of Empirical Accounting Research, 2(3), 1-16. (In Persian)
  20. Moshiri, S., and Maravet, h. (2006). Prediction of total Tehran Stock return index using linear and nonlinear models. Journal of Business Research, 41, 245-276. (In Persian)
  21. Najarzadeh, R; Aghaei Khandabi, M. Rezaei Pour, M. (2009). Investigating the effect of exchange rate and price shocks on price index of the Tehran Stock Exchange by using a vector self-regression approach. Journal of Economic Research, 9(1), 147-175(In Persian)
  22. Noferesti, M. (2011). Statistics: concepts, methods and applications (2nd ed.). Tehran: Resa Publication.
  23. Piraee, and Shahsavar, M. (2009). The effect of macroeconomic variables on stock market in Iran’s stock exchange market. Economic Research, 9(1), 21-38. (In Persian)
  24. Puspanjali, M., Alok, R., and Tapas Kumar, P. (2012). Indian Stock Market Prediction Using Differential Evolutionary Neural Network Model. International Journal of Electronics Communication and Computer Technology (IJECCT), 2(4), 159-166.
  25. Ramezanian, M.R. Ramezanpour, A. and Pourbakhsh, S. H. (2011). New approaches to prediction by using fuzzy-neural networks: oil price. Management Research in Iran. 15(3). 149-167 (In Persian)
  26. Russell, A. and Norwig, P. (2012). Artificial intelligence (a new strategy) (translated by Hadj Rasouliha). Tehran: Niaz-e Danesh Publication (In Persian)
  27. Sadeghi, M. (2011). Artificial intelligence. Tehran: Pardazesh Publication. (In Persian)
  28. Salimifar, M., Razmi, M.J. and Abu Torabi, M.A. (2010). Investigating the causal relationship between financial development and economic growth indicators in Iran. Journal of Quantitative Economics, 7(1), 75-103. (In Persian)
  29. Sa'idi, P and Amiri, E. (2008). Investigating the relationship between macroeconomic variables and total index of Tehran Stock Exchange. Journal of Economic Modeling, 2(2), 130-111. (In Persian)
  30. Sajjadi, S. H; Farazmand, J, and Sufi Hashem, AS. (2010). Investigating the relationship between macroeconomic variables and stock cash return index in Tehran Stock Exchange. Economic Sciences Journal 10(2), 123-150 (In Persian).
  31. Setayesh, M.H. and Kazemnejad, M. (2015). Investigating the utility of nonlinear regression method for support vectors and reduction methods of predictor variable in predicting stock returns. Financial Accounting Quarterly, 7(4), 1-31. (In Persian)
  32. Taheri, T. Nasiri, B. and Meybodi, M. (2012). Improving the efficiency of optimization algorithms in dynamic environments using self-matching Harmony Search algorithm, 15th Iranian Student Conference on Iran’s Electrical Engineering.
  33. Taghilu, E. and Teimuri, E. (2011). Solving the problem of plant production time with Harmonic Search Algorithm. A student conference on Iran’s Electrical Engineering (p. -). Kermanshah: Kermanshah University, Student Scientific Organization of Iran Electrics. adapted from http://www.cirilica.com/paper-ISCEE14-106.html(In Persian)
  34. Thawornwong, S., and Enke, D. (2004). The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing, 56, 205-232.
  35. Torabi, T. Homan T. (2010). Effects of macroeconomic variables on return indexes of Tehran Stock Exchange (TSE) returns indices. Journal of Economic Modeling, 4(1), 121-144. (In Persian)
  36. Vanstone, B., Finnie, G., and Tan, C. (2004). Applying Fundamental Analysis and Neural Networks in the Australian Stockmarket. Information Technology papers.
  37. Vercher. E., J. D. Bermudez (2015). Portfolio optimization using a credibility mean absolute semideviation model, Expert Systems with Applications, 42, 7121-7131. 
  38. Yadav, N., Yadav, Sh., and Dhanda, P. (2013). Stock Price Prediction Using Neural Network. Journal of Harmonized Research (JOHR), 1(2), 146-153.
  39. Yildiz, B., and Yezegel, A. (2010). Fundamental Analysis with Artificial Neural Network. The International Journal of Business and Finance Research, 4(1), 149-158.
  40. Wanjawa, B.W. Muchemi, L. (2014), ANN Model to Predict Prices at Stock Exchange Markets, University of Nairobi,www.ssrn.com  

 

Iranian Journal of Management Studies
Volume 11, Issue 4
Autumn 2018
Pages 769-793

Files

Share

How to cite

Statistics