Application of HS Meta-heuristic Algorithm in Designing a Mathematical Model for Forecasting P/E in the Panel Data Approach

Subject Areas : Financial Accounting

Mozhgan Safa ¹ , Hossein Panahian ²

1 - Department of Accounting, Kashan Branch, Islamic Azad University, Kashan, Iran.
2 - Department Of Accounting , Kashan Branch , Islamic Azad University , Kashan , Iran

Received: 2018-03-19 Accepted : 2018-03-19 Published : 2018-03-01

Keywords: Harmony search, Metaheuristic Algorithms, Forecasting price to earnings (P/E) ratio, Econometric of panel data,

Abstract :

In financial markets such as Tehran Stock Exchange, P/E coefficient, which is one of the most well-known instruments for evaluating stock prices in financial markets, is considered necessary for shareholders, investors, analysts and corporate executives. P/E is used as an important indicator in investment decisions. In this research, harmony search metaheuristic algorithm is used to select optimal variables affecting P/E and then, modelling is done through multivariate regression based on panel data. For this purpose, a sample of 87 companies has been selected from listed companies in the Tehran Stock Exchange during a 10-year period (2006-2015). The results indicate the effect of the variables of stock returns, stock price to book value ratio, price to net selling ratio, return on assets, earnings per share, market value to book value, money volume, operating return margin, return on capital, and current assets, as top ten variables, on P/E ratio, which estimates a total of 86% of the P/E ratio changes.

References:

[1] Amiri Sh., Investigating dynamic correlation of stock price index with 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, 2013.

[2] Piraee Kh., Shahsavar M., Effect of macroeconomic variables on stock market in Iran. Economic research, 2009, 9(1), P.21-38.

[3] Torabi T., Hooman T., Effects of macroeconomic variables on Tehran Stock Exchange (TSE) returns indices. Quarterly Journal of Economic Modelling, 2010, 4(1), P.121-144.

[4] Jalili F., Malek Jafarian M., Safavinejad A., Introducing the harmony search algorithm for optimizing aerodynamic forms using Navier-Stokes equations. Journal of Applied and Computational Sciences in Mechanics, 2013, 2, P.81-96.

[5] Haghighai H., Bakhtiari M., Beheshtipour M.T., Ranking of factors affecting the accuracy of profit forecasting of companies accepted in Tehran Stock Exchange at the time of capital increase. Accounting and Auditing Reviews, 2011, 18(65), P.41-62.

[6] Heydari Zare B., Kordloue H., Stock price forecast using artificial neural network. Management Quarterly, 2010, 17, P.49-56.

[7] Sajjadi S.H., Farazmand J, and Sufi Hashem, A., Investigating the relationship between macroeconomic variables and stock returns index in Tehran Stock Exchange. Economic Research Journal, 2010,10(2), P.123-150.

[8] Saeedi P., Amiri, A., Investigating the relationship between macroeconomic variables and Tehran Stock Exchange Index. Economic Modelling Quarterly, 2008, 2(2), P.111-130.

[9] Salimifar M., Razmi M.J., Abu Tourabi M.A., Investigating the causal relationship between financial development and economic growth indicators in Iran. Quarterly Journal of Economics, 2010, 7(1), P.75-103.

[10] Taheri T., Nasiri B., Meybodi M., Improving the efficiency of optimization algorithms in dynamic environments using self-matching harmony search algorithm. 15th Iranian Student Conference on Electrical Engineering, 2012.

[11] Gujarati D., The Basics of Econometrics (Tenth Edition). Translated by Abrishami, H. Tehran: Tehran University Press, 2011.

[12] Mashayekh Sh., Khamisi H., Farshi Z., Investigating the predictive capability of PEG ratio compared to the P/E ratio to determine stock prices in companies accepted in Tehran Stock Exchange. Quarterly Journal of Empirical Accounting Research, 2013, 2(3), P.1-16.

[13] Nouferasati M., Statistics: Concepts, Methods and Applications (Second Edition). Tehran: Rasa Publications, 2011.

[14] Abdelraouf O., Metwally M.A.B., A Survey of Harmony Search Algorithm.  International Journal of Computer Applications, 2013, 70(28), P.975-987

[15] Budhani N., Jha C. K., Budhani S. K., Application of Neural Network in Analysis of Stock Market Prediction. International Journal of Computer Science & Engineering Technology (IJCSET), 2012, 3(4), P.61-68.

[16] Gao X.Z., Govindasamy V., Xu H., Wang X., Zenger K., Harmony Search Method: Theory and Applications. Hindawi Publishing Corporation Computational Intelligence and Neuroscience, 2015, Article ID 258491, 10 pages

[17] Jolliffe I.T., Principal component Analysis. Springer Series in Statistics, 2^nd Edition, Springer: NewYork, 2002,

[18] Martinez P., Prior D., Rialp J., The price of stocks in Latin American financial markets: An empirical application of the Ohlson model. Global conference on business and finance proceedings, 2012, 7, P.96-100.

[19] Sureshkumar K. K., Elango N. M., An Efficient Approach to Forecast Indian Stock Market Price and their Performance Analysis. International Journal of Computer Applications, 2011, 34(5), P.44-49.

[20] Yildiz B., Yezegel A., Fundamental Analysis with Artificial Neural Network. The International Journal of Business and Finance Research, 2010, 4(1), P.149-158.

[21] Wanjawa B.W., Muchemi L., ANN Model to Predict Prices at Stock ExchangeMarkets, University of Nairobi,www.ssrn.com, 2014.

[22] Liagkouras K., Metaxiotis K., Efficient portfolio construction with the use of multi objective evolutionary algorithms: Best practices and performance metrics, International Journal of Information Technology & Decision Making, 2015, 14, P.535-564.

[23] Asif U., Bhupesh G. Stock Market Trends Prediction Using Neural Network Based Hybrid Model. International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), 2013, 3(1), P.11-18.

[24] Desai J., Trivedi A., Josh N., Forecasting of Stock Market Indices Using Artificial Neural Network. Shri Chimanbhai Patel Institutes, 2013, 3, P.1-18.

[25] Chang T. S., A Comparative Study of Artificial Neural Network, and Decision Trees for Digital Game Content Stock Price Predication. Expert System with Application, 2011, 38, P.14846-14851.