بررسی نظام‌مند مدل‌های پیش‌بینی ورشکستگی
پیشرفت های مالی و سرمایه گذاری
دوره 4، شماره 4 ، آذر 1402، صفحه 144-117 708.26 K )
نوع مقاله: پژوهشی
شناسه دیجیتال (DOI): 10.30495/afi.2023.1971181.1164 نویسندگان
جابر زحمتکش 1 اکرم تفتیان* 1 محمود معین‌الدین 1 امین نظارات 2 1 گروه حسابداری، واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران.2 گروه مهندسی کامپیوتر، واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران.چکیده 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.کلیدواژه‌ها
ابزارهای آماری ؛ ابزارهای هوش مصنوعی ؛ بررسی نظام‌مند ؛ مدل‌های پیش‌بینی ورشکستگی
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