بررسی نظام‌مند مدل‌های پیش‌بینی ورشکستگی

محورهای موضوعی : سرمایه‌گذاری

جابر زحمتکش ¹ , اکرم تفتیان ² , محمود معین‌الدین ³ , امین نظارات ⁴

1 - گروه حسابداری، واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران.
2 - گروه حسابداری، واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران.
3 - گروه حسابداری، واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران.
4 - گروه مهندسی کامپیوتر، واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران.

تاریخ دریافت : 781/01/23 تاریخ پذیرش : 781/05/10 تاریخ انتشار : 781/07/02

کلید واژه: مدل‌های پیش‌بینی ورشکستگی, بررسی نظام‌مند, ابزارهای هوش مصنوعی, ابزارهای آماری,

چکیده مقاله :

هدف: هدف پژوهش حاضر بررسی نظام‌مند مدل‌های پیش‌بینی ورشکستگی در راستای ایجاد مدلی است که به‌عنوان راهنمایی برای انتخاب ابزار مناسب که بهترین انطباق را با داده‌های موجود و معیارهای کیفیت مدل‌های پیش‌بینی ورشکستگی دارد عمل کند.روش‌شناسی پژوهش: برای انجام این پژوهش، جستجوی سیستماتیک از database (web of Science) با استفاده از کلیدواژه‌های Bankruptcy، Default، Distress، Failure، Forecasting، Predicting، Prediction و Insolvency بین سال‌های ۲۰15 لغایت 2023 صورت پذیرفت. باتوجه‌به معیارهای ورود و خروج تعریف‌شده، حاصل این جستجو 1000 مقاله بود که درنهایت 49 مقاله از میان آن‌ها انتخاب و مورد تجزیه‌وتحلیل قرار گرفت. سپس یافته‌های به‌دست‌آمده از مقالات، در جداول خلاصه‌سازی وارد گردیده و در گام بعدی، مدل‌های بزرگ پیش‌بینی ورشکستگی بر اساس 9 معیار کلیدی با یکدیگر مقایسه و نتیجه‌گیری نهایی به عمل آمد.یافته‌ها: شبکه عصبی مصنوعی و ماشین‌های بردار پشتیبان دارای بیشترین دقت می‌باشند درحالی‌که تحلیل شخصیتی چندگانه دارای کمترین دقت است. همچنین شبکه عصبی مصنوعی و تحلیل شخصیتی چندگانه، درخت تصمیم‌گیری و رگرسیون لجستیک به نمونه آموزشی بزرگی نیاز دارند تا الگویی را منطقاً شناسایی کرده و طبقه‌بندی بسیار دقیقی ارائه دهند؛ اما استدلال مبتنی بر مورد، مجموعه‌های راف و ماشین‌های بردار پشتیبان می‌توانند با اندازه نمونه‌های کوچک کار کنند.اصالت / ارزش‌افزوده علمی: نتایج این پژوهش به درک کامل ویژگی‌های ابزارهای مورداستفاده برای توسعه مدل‌های پیش‌بینی ورشکستگی و کاستی‌های مربوط به آن‌ها کمک می‌کند.

چکیده انگلیسی:

Objective: The current research aims to systematically examine bankruptcy prediction models with the goal of developing a model that serves as a guide for selecting the most suitable tools. These tools should ideally align with the existing data and quality criteria of bankruptcy prediction models.Research Methodology: To conduct this research, a systematic search was performed on the Web of Science database using keywords such as Bankruptcy, Default, Distress, Failure, Forecasting, Predicting, Prediction, and Insolvency, spanning the years 2015 to 2023. Based on defined inclusion and exclusion criteria, this search yielded 1000 articles, out of which 49 were ultimately selected and analyzed. The findings from these articles were then summarized in tables. Subsequently, major bankruptcy prediction models were compared based on nine key criteria, and final conclusions were drawn.Findings: Artificial neural networks and support vector machines were found to have the highest accuracy, while multiple personality analysis showed the lowest accuracy. Additionally, artificial neural networks, multiple personality analysis, decision trees, and logistic regression require a large training sample to logically identify and precisely classify patterns. However, case-based reasoning, rough sets, and support vector machines can work with smaller sample sizes.Originality/ Value: The outcomes of this research contribute to a comprehensive understanding of the characteristics of tools used in developing bankruptcy prediction models and the shortcomings associated with them.

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