بررسی توانایی شاخص های حسابداری و غیرحسابداری مؤثر بر پیش‌بینی درماندگی مالی و مقایسه روشهای پارامتریک و ناپارامتریک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار گروه حسابداری، دانشکده مدیریت، دانشگاه تهران، تهران، ایران

2 دانشیار گروه حسابداری، دانشکده علوم اقتصادی و اداری، دانشگاه مازندران، بابلسر، ایران

3 دانشجوی دکتری حسابداری، دانشکده علوم اداری و اقتصادی، دانشگاه مازندران، بابلسر، ایران

چکیده

هدف این تحقیق، بررسی توانایی شاخصهای حسابداری و غیرحسابداری مؤثر بر پیش‌بینی درماندگی مالی و مقایسه روشهای پارامتریک و ناپارامتریک است. بدین منظور اطلاعات 211 سال-شرکت درمانده منتخب بر اساس معیارهای خاص درماندگی و 211 سال-شرکت سالم پذیرفته‌شده در بورس اوراق بهادار تهران در فاصله بین سالهای 1384 الی 1393 مورد استفاده قرار گرفته‌است. در این مطالعه از 32 شاخص حسابداری و 20 شاخص غیرحسابداری به همراه دو روش پارامتریک شامل روشهای رگرسیون لوجستیک و تحلیل ممیزی چندگانه و هفت روش ناپارامتریک شامل ماشین بردار پشتیبان، شبکه عصبی مصنوعی، درخت تصمیم (با 4 الگوریتم) و شبکه بیزین جهت پیش‌بینی درماندگی مالی استفاده گردیده است. نتایج تحقیق نشان می‌دهد مدلهای مستخرج از شاخصهای حسابداری به طور معنی‌داری نسبت به مدلهای مبتنی بر شاخصهای غیرحسابداری از دقت پیش‌بینی بالاتری برخوردارند و اضافه نمودن شاخصهای غیرحسابداری به مدلهای مبتی بر شاخصهای حسابداری، قدرت پیش‌بینی آنها را به طور معنی‌داری افزایش نمی‌دهد. همچنین، علیرغم بالاتر بودن میانگین توانایی پیش‌بینی روشهای ناپارمتریک نسبت به روشهای پارامتریک، این تفاوت از نظر آماری معنی‌دار نیست.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Effects of Accounting and Non-Accounting Indices on Financial Distress Prediction: Comparing Parametric and Non-parametric Methods

نویسندگان [English]

  • Sasan Mehrani 1
  • yahya kamyabi 2
  • farzad ghayour 3
1 Associate Professor of Accounting, Tehran university, Tehran, Iran
2 Associate Professor of Accounting, Mazandaran university, Babolsar, Iran
3 Phd. student of Accounting, Mazandaran university, Babolsar, Iran
چکیده [English]

This study aims to investigate the effects of accounting and non-accounting indices on financial distress prediction and also to compare parametric and non-parametric methods. Therefore, the sample consists of 211 distressed firms selected by special distress criteria and 211 healthy firms listed in Tehran Stock Exchange during 2006-2015. This study applies 32 accounting and 20 non-accounting indices and uses 2 parametric methods including Logistic Regression and Multivariate Discriminate Analysis and 7 non-parametric methods including Support Vector Machine, Artificial Neural Network, Decision Tree (with 4 algorithms) and Bayesian Network to predict financial distress. The results show that the models extracted from accounting indices have significantly more predicting accuracy than those from non-accounting indices, and adding non-accounting indices to the models based on accounting indices does not significantly increases their predicting ability. Also, since the average predicting ability of non-parametric methods is more than parametric ones, this difference is not statistically significant.

کلیدواژه‌ها [English]

  • Financial Distress
  • Accounting Indices
  • Non-accounting Indices
  • Parametric Methods
  • Non-Parametric Methods
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