پیش‌بینی ریسک ورشکستگی مالی با استفاده از مدل ترکیبی در بورس اوراق بهادار تهران

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار گروه مدیریت بازرگانی، دانشگاه قم

2 کارشناسی‌ارشد مدیریت بازرگانی گرایش مالی، دانشکده مدیریت دانشگاه قم

چکیده

پیش‌بینی ریسک ورشکستگی مالی یکی از مهمترین موضوعات در حوزه تصمیم‌گیری مالی شرکت‌ها است. از این جهت، تاکنون مدل‌های متنوعی که هرکدام از نظر متغیرهای پیش‌بینی‌کننده و تکنیک‌ها متفاوتند، ارائه شده‌اند. استفاده از ترکیب متغیرهای حسابداری و بازاری در مدل به عنوان ورودی، قطعاً بر نتایج و دقت پیش‌بینی‌ها تاثیر مستقیمی خواهد داشت. در این مطالعه، پیش‌بینی با استفاده از مدل ترکیبی (استفاده از متغیرهای حسابداری و بازاری ) و تکنیک شبکه‌های عصبی از نوع مدل پرسپترون چندلایه (MLP) صورت پذیرفت. نمونه پژوهش شامل 90 شرکت پذیرفته شده در بورس اوراق بهادار تهران (31 شرکت ورشکسته طبق ماده 141 قانون تجارت ایران و 59 شرکت غیرورشکسته) طی سال‌های 1393-1386 می‌باشد. نتایج پژوهش نشان می‌دهد که مدل ترکیبی (ترکیب متغیرهای حسابداری و بازاری) با استفاده از تکنیک شبکه عصبی، نسبت به هر کدام از دو مدل حسابداری و بازاری از دقت بالاتری در پیش‌بینی ریسک ورشکستگی مالی برخوردار است. همچنین، مدل بازاری نیز دقت بیشتری نسبت به مدل حسابداری دارد.

کلیدواژه‌ها


عنوان مقاله [English]

The Prediction of the Risk of Financial Bankruptcy Using Hybrid Model in Tehran Stock Exchange

نویسندگان [English]

  • Najmeh Ramooz 1
  • Maryam Mahmoudi 2
1 qom university
2 qom university
چکیده [English]

Predicting the risk of financial bankruptcy is one of the most important issues in the field of companies’ financial decision. Accordingly, a variety of models that each is different in terms of predictor variables and techniques has been introduced so far. The use of the combination of accounting and market-driven variables in the model as input will have definitely a direct impact on the results and accuracy of forecasts. In this study, the prediction was accomplished by using a hybrid model (the use of accounting and market-driven variables) and neural networks technique of multi-layer perceptron model (MLP). The sample of research consists of 90 accepted companies in Tehran Stock Exchange (31 bankrupted companies in accordance with article Iran’s 141 trade laws and 59 non-bankrupted companies) during 2007-2014 period. The research results show that the hybrid model (combination of accounting and market-driven variables) using neural network technique has higher accuracy than each of the two accounting models and market-driven model in predicting the risk of financial bankruptcy. Likewise, the market-driven model is more accurate than accounting model.

کلیدواژه‌ها [English]

  • Risk of Financial Bankruptcy
  • Accounting Variables
  • Market-Driven Variables
  • Hybrid Model
  • Neural Network
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