EPS forecast modeling using neural networks - Fuzzy

Subject Areas :

A.A Anvari ¹ , عادل Azar ² , محمد Norozi ³

1 - استاد گروه حسابداری دانشگاه تربیت مدرس
2 - استاد گروه مدیریت صنعتی دانشگاه تربیت مدرس
3 - کارشناس ارشد مدیریت بازرگانی (مالی) دانشگاه تربیت مدرس

Received: 2015-05-06 Accepted : 2015-05-06 Published : 2014-11-22

Keywords:

Abstract :

Earnings per share prediction and its changes as an economic events, past, were interested for investors, managers, financial analysts and creditors. This interest is because of the use of earnings in share valuation models, improving efficient performing of capital markets, and evaluating solvency and evaluating of firm performance. The purpose of this paper is to earnings per share prediction using neural-fuzzy networks, MLP, GMDH, and determine most preferable model using four measures of evaluating performance. So, companies listed in TSE was chosen as statistical population and statistical sample is consisted of 500 firm-year 24 active industry 1386 to 1390 were chosen randomly using clustering sampling. The results show that neural-fuzzy networks is the most preferable comparing with neural networks, MLP, and GDMH, in all of four measures of evaluating performance, that it is showing of high power of this kind of networks in identifying dominant patterns of data and existence of non-liner relations of some accounting variables with EPS. So, the accuracy of neural-fuzzy networks predictions is more than MLP and GDMH, and is more suitable for EPS prediction.

References:

 

• انواری رستمی، علی‌اصغر. (1378)، "مدیریت مالی و سرمایه‌گذاری"، تهران: انتشارات طراحان نشر.


• خدادادی، ولی و رضا جان‌جانی، (1390)،" بررسی رابطه مدیریت سود و سودآوری شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران"،مجله پژوهش‌های حسابداری مالی،ش1، صص۷۷-96.


• مهام، کیهان، (1379)،" اثر گزارش اجزای سود حسابداری  برافزایش توان پیش‌بینی سود"، پایان‌نامه دکترای رشته حسابداری، دانشگاه علامه طباطبائی.


• Abarbanell, J.S., & Bushee, B.J.(1997). Fundamental Analysis, Future Earnings, and Stock Prices. Journal of Accounting Research, 35(1),PP. 1-24.


• Atashkari, K., Nariman-Zadeh, N., Gölcü, M., Khalkhali, A., & Jamali, A. (2007). Modelling and Multi-Objectiveoptimization of a Variable Valve-Timing Spark-ignition Engine Using Polynomial Neural Networks and Evolutionary Algorithms. Energy Conversion and Management, 48(3),PP. 1029-1041.


• Atsalakis, G.S., Dimitrakakis, E.M., & Zopounidis, C.D. (2011). Elliott Wave Theory and Neuro-Fuzzy Systems, in Stock Market Prediction: The WASP system. Expert Systems With Applications, 38(8), PP.9196-9206.


• Callen, J.L., Kwan, C.C.Y., Yip, P.C.Y., & Yuan, Y. (1996). Neural Network Forecasting of Quarterly Accounting Earnings. International Journal of Forecasting, 12(4),PP. 475-482.


• Cao, Q., & Gan, Q. (2009). Forecasting EPS of Chinese Listed Companies Using Neural Network with Genetic Algorithm.


• Cheng, C.H., Hsu, J.W., & Huang, S.F. (2009). Forecasting Electronic Industry EPS Usingan Integrated ANFIS Model. Paper Presented at the Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on.


• Graham, B. (1934). Security Analysis. School of Business, Columbia University.  


• Ivakhnenko, AG, & Müller, J.A. (1995). Present State and new Problems of Further GMDH Development. Systems Analysis Modelling Simulation, 20(1-2),PP. 3-16.


• Khashei, Mehdi, & Bijari, Mehdi. (2010). A Novel Hybridization of Artificial Neural Networks and ARIMA Models for Time Series Forecasting. Applied Soft Computing.


• Khatibi, R., Ghorbani, M.A., Kashani, M.H., & Kisi, O. (2011). Comparison of Three Artificial Intelligence Techniques for Discharge Routing. Journal of Hydrology, 403(3),PP. 201-212.


• Lev, B., & Thiagarajan, S.R. (1993). Fundamental Information Analysis. Journal of Accounting Research,PP. 190-215.


• Lubis, H.Y., & Director-Donnell, M. (2000). Initial Public Offering Prediction using Neural Network: The George Washington University.


• Madala, H.R., & Ivakhnenko, A.G. (1994). Inductive Learning Algorithms for Complex Systems Modeling: CRC Press Boca Raton, FL.


• Malinowski, P., & Ziembicki, P. (2006). Analysis of District Heating Network Monitoring by Neural Networks Classification. Journal of Civil Engineering and Management, 12(1),PP. 21-28.


• Mantas, CJ, Puche, JM, & Mantas, JM. (2006). Extraction of Similarity basedFuzzy rules Artificial Neural Networks. International Journal of Approximate Reasoning, 43(2),PP. 202-221.


• McCleary, R., & Hay, R.A. (1980). Applied Time Series Analysis for The Social Sciences: Sage Publications Beverly Hills, CA.


• Noori, R., Hoshyaripour, G., Ashrafi, K., & Araabi, B.N. (2010). Uncertainty Analysis of Developed ANN and ANFIS models in Prediction of Carbon Monoxide Daily Concentration. Atmospheric Environment, 44,PP.476-482


• Patterson, Dan W. (1998). Artificial Neural Networks: Theory and Applications: Prentice Hall PTR.


• Sumathi, Sai. (2009). Computational Intelligence Paradigms: Theory & Applications Using MATLAB: CRC.


• Thomas, J. K. and Zhang, H. (2002). “Inventory Changes and Future Returns” Review of Accounting Studies. No. 7, PP.163-187


• Takagi, T., & Sugeno, M. (1985). Fuzzy Identification of Systems and its Applications to Modeling and Control. Systems, Man and Cybernetics, IEEE Transactions on(1),PP.116-132.


• Zhang, G.P. (2003). Time Series Forecasting using a Hybrid ARIMA and Neural Network Model. Neurocomputing, 50,PP.159-175.