Forecasting the Profitability in the Firms Listed in Tehran Stock Exchange Using Data Envelopment Analysis and Artificial Neural Network
Publish Year: 1395
نوع سند: مقاله ژورنالی
زبان: English
View: 260
This Paper With 10 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_AMFA-1-2_008
تاریخ نمایه سازی: 7 مهر 1400
Abstract:
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 ۲۰۱۲ to ۲۰۱۵of ۷ 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.
Keywords:
Authors
Maryam Saberi
Department of Management and Accounting, Tarbiat Modares University,Tehran, Iran
Mohammad Reza Rostami
Department of Management and Accounting, Tarbiat Modares University,Tehran, Iran
Mohsen Hamidian
Department of Economics and Accounting, Islamic Azad University South Tehran Branch, Iran
Nafiseh Aghami
Department of Management and Accounting, Al-Zahra University, Tehran, Iran.
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :