EPS forecast modeling using neural networks - Fuzzy

Subject Areas :

A.A Anvari ¹ (استاد گروه حسابداری دانشگاه تربیت مدرس)
عادل Azar ² (استاد گروه مدیریت صنعتی دانشگاه تربیت مدرس)
محمد Norozi 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.

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