خلاصة:
Profitability as the most important factor in decision-making, has always been
considered by stakeholders in the company's profitability. Also can be a basis
for evaluating the performance of the managers. The ability to predict the
profitability can be very useful to help decision-makers. That's why one of the
most important issues is the expected profitability. The importance of these
forecasts depends on the amount of misalignment with reality. The amount of
deviation is less than the forecast of higher accuracy. Although there are various
methods for predicting but the use of artificial intelligence techniques is
increasing due to fewer restriction. The aim of this study is to evaluate the
predictive power of profitability using DEA and neutral network, to enhance the
decision-making users of 2012 to 2015of 7 premier financial ratios were used as
independent variables. Test results show that both of ANN and DEA have ability
to forecast profitability and given that neutral network prediction accuracy is
higher than the DEA, the model predict better the profitability of companies.
ملخص الجهاز:
Forecasting the Profitability in the Firms Listed in Tehran Stock Exchange Using Data Envelopment Analysis and Artificial Neural Network Maryam Saberi a,*, Mohammad Reza Rostamia, Mohsen Hamidianb, Nafiseh Aghamic a Department of Management and Accounting, Tarbiat Modares University,Tehran, iran b Department of Economics and Accounting, Islamic Azad University South Tehran Branch, iran c Department of Management and Accounting, Al-Zahra University, Tehran,iran ARTICLE INFO Article history: Received 12 Septamber 2016 Accepted 29 December 2016 Keywords: ABSTRACT Artificial Neural Network(ANN), Fuzzy DEA, Earnin predicting, Decision Making.
The aim of this study is to evaluate the predictive power of profitability using DEA and neutral network, to enhance the decision-making users of 2012 to 2015of 7 premier financial ratios were used as independent variables.
Test results show that both of ANN and DEA have ability to forecast profitability and given that neutral network prediction accuracy is higher than the DEA, the model predict better the profitability of companies.
In this way, decision makers can quantitatively evaluate the relative performance of each oil and gas firm based on reserve acquisition, exploration and development activity information, which is provided in the footnotes to the financial reports.
Unlike change in asset turnover, our DEA based efficiency measure is useful in predicting future profitability for both FC firms and SE firms of the oil and gas industry.
The diagnostic measure predicts differences of firms’ future profitability and stock returns, indicating the usefulness of information on earnings quality in forecast models [2].