پیش‌بینی درماندگی مالی بر مبنای تئوری مجموعه‌های راف در مقایسه با شبکه‌های عصبی مصنوعی
فصلنامه بورس اوراق بهادار

فصلنامه بورس اوراق بهادار

پیش‌بینی درماندگی مالی بر مبنای تئوری مجموعه‌های راف در مقایسه با شبکه‌های عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 کارشناسی ارشد، گروه مدیریت مالی، دانشگاه ارومیه، ارومیه، ایران.
2 دانشیار، گروه حسابداری، دانشگاه ارومیه، ارومیه، ایران.
3 استادیار، گروه حسابداری، دانشگاه ارومیه، ارومیه، ایران
چکیده
در عصری که شرکت‌ها برای بقا در بازارهای رقابتی با چالش‌های فراوانی روبه‌رو هستند، شناسایی عوامل تاثیرگذار بر بحران‌های مالی اهمیت می‌یابد. یکی از راه‌های کمک به سرمایه-گذاران و شرکت‌ها ارائه، الگوهایی جهت پیش‌بینی درماندگی مالی است. هدف این پژوهش، بررسی توانایی تئوری مجموعه راف و مقایسه آن با شبکه‌های عصبی مصنوعی و فازی جهت پیش‌بینی درماندگی مالی شرکت‌های فعال در بورس اوراق بهادار تهران است. بدین منظور 329 جفت شرکت سالم و درمانده مالی طی بازه زمانی سال‌های 1385-1399 انتخاب شده‌است. شبکه-های عصبی بررسی شده در این پژوهش عبارتنداز: شبکه عصبی پرسپترون چندلایه (MLP) و شبکه عملکرد پایه شعاعی (RBF) و شبکه استنتاج فازی انطباق‌پذیر(ANFIS) و همچنین نرم افزار مورد استفاده جهت ایجاد تئوری مجموعه راف ROSETTA و نرم‌افزار مورد استفاده جهت طراحی شبکه‌های عصبی مصنوعی و فازی نرم افزار MATLAB می‌باشد. نتایج به دست آمده در این پژوهش نشان از کارایی بالای مدل مجموعه‌های راف با دقت 7/98درصد جهت پیش‌بینی درماندگی مالی دارد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Prediction of financial distressed based on the theory of rough sets compared to artificial neural networks

نویسندگان English

Seyed Amirmohamad Hejaziyan 1
Gholamreza Mansourfar 2
Farzad Ghayour 3
1 M.Sc. Department of Financial Management, Urmia University, Urmia, Iran
2 Associate Professor, Department of Accounting, Urmia University, Urmia, Iran.
3 Assistant Professor, Department of Accounting, Urmia University, Urmia, Iran
چکیده English

In the era when companies are facing many challenges to survive in competitive markets, it is important to identify the factors affecting financial crises. One of the ways to help investors and companies is to provide models to predict financial distress. The purpose of this research is to investigate the ability of rough set theory and compare it with artificial and fuzzy neural networks to predict the financial distress of companies active in the Tehran Stock Exchange. For this purpose, 329 distressed companies from non-distressed corporations were selected during the period of 2006 to 2020. The neural networks investigated in this research are: Multi-layer Perceptron (MLP) neural network, Radial Basis Function (RBF) network, and Adaptive Fuzzy Inference Network (ANFIS) as well as the software used to create the ROSETTA rough set and the software The tool used to design artificial and fuzzy neural networks is MATLAB software. The results obtained in this research show the high efficiency of the rough sets model with 98/7 percent accuracy for predicting financial distress.

کلیدواژه‌ها English

Financial Distressed
Rough Set Theory
Artificial neural network
Predicting
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