پیش‌بینی درماندگی مالی شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران با استفاده از تکنیک DEA-DA و شبکه‌ی عصبی مصنوعی

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسندگان

1 گروه مدیریت صنعتی ، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران

2 دانشیاربخش تخصصی مدیریت صنعتی و فناوری، دانشکده مدیریت و حسابداری، پردیس فارابی دانشگاه تهران

10.48301/kssa.2021.275411.1408

چکیده

هدف این پژوهش شناسایی شاخص‌های مالی جهت ارزیابی و تجزیه و تحلیل درماندگی مالی شرکت­های پذیرفته‌شده در بورس اوراق بهادار تهران و سپس پیش­بینی پویای درماندگی مالی شرکت‌ها است. بدین منظور ﭘـﺲ از ﻣﻄﺎﻟﻌــﻪ‌ی ﺟــﺎﻣﻊ ادﺑﻴــﺎت ﺗﺤﻘﻴــﻖ و ﺑﺮرﺳــﻲ نسبت‌های ﻣــﺎﻟﻲ ﻣﻬــﻢ مورداستفاده در پژوهش‌های ﻗﺒلی، تعداد هشت ﻧﺴﺒﺖ ﻣﺎﻟﻲ ﻛﻪ ﺑﻴﺶ از ﻫﻤﻪ در ﺗﺤﻘﻴﻘـﺎت ﻗﺒﻠـﻲ مورداستفاده قرارگرفته ﺑﻮد، اﻧﺘﺨﺎب ﺷﺪﻧﺪ و داده‌های موردنیاز تحقیق از منابع اطلاعاتی سازمان بورس و اوراق بهادار و سامانه‌های دادگان موجود مانند نرم‌افزار سازمان بورس اوراق بهادار تهران، کدال و ره‌آورد نوین  برای 106 شرکت گردآوری گردید. سپس فرآیند خوشه‌بندی برای 105 شرکت با کمک روش شبکه‌ی عصبی مصنوعی SOM انجام‌شده که در این پژوهش تعداد خوشه‌های موجود برابر با دو خوشه (شرکت­های درمانده و غیر­ درمانده) در نظر گرفته‌شده است. پس از خوشه‌بندی شرکت‌ها، مدل ارائه‌شده DEA-DA اجرا و درنهایت عضویت شرکت جدید در گروه مناسب درمانده یا غیر درمانده پیش‌بینی گردید. نتایج حاصل از این پژوهش نشان می‌دهد که  عضویت شرکت جدید در خوشه‌ی درمانده‌ی مالی،  به‌درستی پیش‌بینی‌شده و روش ارائه‌شده در این پژوهش با در نظر گرفتن انواع معیارهای اقتصادی و مالی، امکان پویاسازی پیش‌بینی درماندگی مالی را برای تصمیم‌گیرندگان ازجمله مدیران شرکت‌ها و سرمایه‌گذاران فراهم می‌سازد. 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Predicting the Financial Distress of Companies Listed on the Tehran Stock Exchange Using DEA-DA Technique and Artificial Neural Network

نویسندگان [English]

  • Hamid Rahimi 1
  • Mehrzad Minooe 1
  • Mohammad Reza Fathi 2
1 Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 Associate Professor, College of Farabi, University of Tehran, Iran
چکیده [English]

This study aims to identify financial criteria to evaluate and analyze the financial distress of companies listed on the Tehran Stock Exchange and the dynamic forecast of corporate financial distress. Therefore, after a comprehensive review of the research literature and the main financial ratios used in previous studies, eight financial ratios widely used in previous research were selected. The research data was collected from the Exchange and Securities Organization data sources and existing data systems such as Tehran Stock Exchange, Codal.ir website, and Rahvard Novin software related to 106 companies. Then, the clustering process was performed for 105 companies using the SOM artificial neural network method. In this study, the number of existing clusters was considered equal to two clusters (financially distressed and non-distressed companies). After clustering the companies, the proposed DEA-DA model was implemented. Finally, the membership of the new company was predicted in the appropriate distressed or non-distressed cluster. The study's results indicated that the membership of the new company in the financial distress cluster was correctly predicted, and the proposed method made it possible to dynamize the financial distress forecast for decision-makers including corporate managers and investors by considering various economic and financial criteria.

کلیدواژه‌ها [English]

  • Financial distress Data envelopment analysis
  • Discriminant analysis (DEA
  • DA) Artificial neural networks Self
  • organizing map (SOM)

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فصلنامه علمی کارافن
دوره 19، شماره 2 - شماره پیاپی 58
علوم انسانی
شهریور 1401
صفحه 521-549

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  • تاریخ دریافت: 16 اسفند 1399
  • تاریخ بازنگری: 10 شهریور 1400
  • تاریخ پذیرش: 27 شهریور 1400

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