برآورد الگوی پیش بینی درماندگی مالی مبتنی بر تلفیق الگوریتم بردار ماشین و الگوی حداقل مربعات
کلمات کلیدی:
الگو, تکنیکهای یادگیری ماشینی, غیرخطی بودن, همبستگیهای پیچیده, ورشکستگیچکیده
هدف: هدف از این پژوهش، ارائه یک مدل ترکیبی مبتنی بر حداقل مربعات جزئی (PLS) و ماشین بردار پشتیبان (SVM) برای پیشبینی درماندگی مالی شرکتها و بهبود دقت و پایداری فرآیند پیشبینی است. روششناسی: این پژوهش از یک مجموعه داده شامل 120 شرکت، متشکل از 56 شرکت ورشکسته و 64 شرکت غیرورشکسته، در بازه زمانی دو ساله استفاده کرده است. ابتدا دادههای مالی مورد تجزیه و تحلیل قرار گرفتند و ویژگیهای مهم با استفاده از روش حداقل مربعات جزئی (PLS) استخراج شدند. سپس از الگوریتم ماشین بردار پشتیبان (SVM) با استفاده از روش جستجوی شبکهای و اعتبارسنجی متقاطع 5 بخشی برای تنظیم بهینه پارامترهای مدل استفاده شد. عملکرد مدل پیشنهادی با روشهای سنتی مانند رگرسیون لجستیک و شبکههای عصبی مصنوعی مقایسه گردید. یافتهها: نتایج تجربی نشان دادند که مدل ترکیبی PLS-SVM با نرخ دقت 87 درصد در مجموعه آزمایشی، عملکرد بهتری نسبت به مدلهای سنتی و سایر تکنیکهای یادگیری ماشین دارد. همچنین، این مدل توانست مرتبطترین شاخصهای مالی را برای پیشبینی درماندگی مالی شناسایی کرده و تأثیر هر یک از متغیرها را در فرآیند پیشبینی مشخص نماید. نتیجهگیری: مدل پیشنهادی به دلیل دقت بالا، تفسیرپذیری مناسب، و پایداری قابل توجه، میتواند به عنوان یک ابزار مؤثر برای مؤسسات مالی در فرآیندهای مدیریت ریسک، تأیید اعتبار، و برنامهریزی مالی به کار رود. این پژوهش نشان میدهد که ترکیب روشهای یادگیری ماشین میتواند به بهبود قابلیتهای پیشبینی مالی کمک کند.
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حق نشر 2025 Gholamhasan Taghizad, Hossein Panahian, Hasan Ghodrati (Author)

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