Determination of Financial Failure Indicators by Gray Relational Analysis and Application of Data Envelopment Analysis and Logistic Regression Analysis in BIST 100 Index

Document Type: Research Paper

Authors

Department of Business Administration, Faculty of Economics and Administrative Sciences, Akdeniz University, Antalya, Turkey

Abstract

Financial failure prediction models have been developed by using Logistic Regression (LR) analysis from traditional statistical methods and Data Envelopment Analysis (DEA), which is a mathematically based nonparametric method over the financial reports of the companies traded in The Istanbul Stock Exchange National 100 Index (BIST 100) between the years 2014-2016. In the development of these models, the variables included in the model are as important as the method applied. For this reason, the gray relational analysis method has been considered in determining the indicators that affect the financial situation of the companies. As a result of the analysis, it was determined that the LR model, which is one of the prediction models, has a higher rate of prediction power than the data envelopment analysis in predicting the financial failure of the companies. However, DEA is also an easy and fast method for predicting financial failures, and is recommended to companies on the indicators that they need to improve in order to be successful. As a result of the study, it has been found that both methods are feasible in the prediction of financial failure, but these methods also have different advantages and disadvantages.

Keywords

Main Subjects


Article Title [فارسی]

تعیین شاخص های شکست مالی با استفاده از تحلیل رابطه خاکستری مبتنی بر تحلیل پوششی داده ها و تحلیل رگرسیون لجستیک در شاخص بورس بیست 100

Authors [فارسی]

  • ابرو نورجان
  • جان دنیز کوکسال
گروه مدیریت بازرگانی، دانشکده علوم اقتصادی و مدیریت، دانشگاه آکدنیز، آنتالیا، ترکیه
Abstract [فارسی]

مدل های پیش بینی شکست مالی با استفاده از تحلیل رگرسیون لجستیک بر مبنای روشهای سنتی آماری و تحلیل پوششی داده ها توسعه یافته اند که یک روش ناپارامتریک ریاضی-محور است. در این مقاله، این روش روی گزارش های مالی شرکت های حاضر در شاخص 100 شرکت بزرگ بورس استانبول (بیست 100) بین سالهای 2014 تا 2016 به کار گرفته شده است. برای تدوین این مدلها، متغیرهای لحاظ شده در مدل به اندازه روش مورد استفاده مهم هستند. به همین دلیل، روش تحلیل رابطه خاکستری برای تعیین شاخص های موثر بر وضعیت مالی شرکت ها مورد استفاده قرار گرفت. نتایج به دست آمده نشان داد که مدل رگرسیون لجستیک، به عنوان یک مدل پیش بینی، نرخ پیش بینی بالاتری از روش تحلیل پوششی داده ها در پیش بینی شکست مالی شرکت ها دارد. با این حال، تحلیل پوششی داده ها هم یک راه آسان و سریع برای پیش بینی شکست مالی بوده و به شرکت هایی توصیه می شود که برای موفقیت نیاز به بهبود شاخص های مشخص شده دارند. به علاوه، ما دریافتیم که هر دو روش در پیش بینی شکست مالی قابل اجرا هستند، اما هر یک دارای مزایا و معایب خاص خود هستند.

Keywords [فارسی]

  • شکست مالی
  • تحلیل رابطه خاکستری
  • تحلیل پوششی داده ها
  • رگرسیون لجستیک
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