پیش‌بینی ریسک مالی شرکت‌ها بر اساس الگوریتم ترکیبی یادگیری عمیق FA-PSO-LSTM

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد مدیریت مالی، دانشکده اقتصاد و علوم اجتماعی، دانشگاه شهید چمران اهواز. اهواز . ایران

2 گروه مدیریت- دانشکده اقتصاد و علوم اجتماعی- دانشگاه شهید چمران اهواز،اهواز، ایران

3 استاد گروه مدیریت، دانشکده اقتصاد و علوم اجتماعی، دانشکاه شهید چمران هواز، اهواز، ایران

چکیده

این پژوهش با توجه به لزوم نظارت مستمر یر داده های مالی و با تمرکز بر الگوریتم های هوش مصنوعی طی یک مطالعه تجربی و میدانی و با استفاده از با استفاده از یک مدل جدید مورد، داده های شرکت های فعال در سه صنعت فلزات اساسی، خودروسازی و فرآورده های نفتی را در یک بازه زمانی ده ساله موردارزیابی دقیق قرار داده است. پس از تعیین عوامل موثر مالی حاصل از تحلیل عاملی، از خطای میانگین مربعات و مقادیر پیش بینی شده شبکه عصبی LSTM جهت بهینه سازی تابع الگوریتم ازدحام ذرات و بهینه سازی نرخ یادگیری و تعداد لایه های پنهان شبکه های عصبی استفاده گردید. مدل یادگیری عمیق FA-PSO-LSTM مورد استفاده یک مدل ابتکاری و نسبتا جدید است که از مزایای شبکه LSTM در پردازش سری های زمانی به طور کامل می تواند بهره مند شده و به تکامل نطریه پردازی در این خصوص منتهی شود. نتایج حاکی از آن است که مدل پیشنهادی در صنعت فرآورده‌های نفتی با دقت بالا ریسک مالی را پیش‌بینی نموده و اهمیت متغیرهایی چون نقدینگی، جریان نقدی و سودآوری را برجسته ساخته است. در صنعت خودروسازی، شاخص‌هایی مانند نقدینگی، ظرفیت عملیاتی و توسعه پایدار اثرگذارتر بودند، در حالی که در صنعت فلزات اساسی بیشترین ثبات داده‌ها مشاهده و بهترین عملکرد مدل ثبت شد.در مجموع، متغیرهای نقدینگی، سودآوری، جریان نقدی، ظرفیت عملیاتی و قابلیت توسعه به‌عنوان عوامل کلیدی مشترک ریسک شناسایی گردیدند همچنین مشخص شد که که مدل پیشنهادی در مقایسه با روش‌های سنتی و سایر الگوریتم‌ها، دقت و کارایی بالاتری دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Predicting the Financial Risk of Public Companies with a Hybrid Algorithm FA-PSO-LSTM

نویسندگان [English]

  • Fatemeh Sadat Mirmoeini 1
  • Rahim Ghasemiyeh 2
  • Hasan Ali Sinaie 3
1 MS in Financial Management. Faculty of Economics and Social Sciences, Shahid Chamran University of Ahvaz. Ahvaz. Iran
2 Department of management, Faculty of Economics and Social Sciences, Ahvaz, Iran
3 Professor, Faculty of Economics and Social Sciences, Department of Management, Shahid Chamran university of Ahvaz, Ahvaz, Iran
چکیده [English]

This research, considering the need for continuous monitoring of financial data and focusing on artificial intelligence algorithms, has carefully evaluated the data of companies active in the three industries of basic metals, automotive, and petroleum products over a ten-year period using an experimental and field study and a new case model. After determining the effective financial factors from factor analysis, the mean square error and predicted values of the LSTM neural network were used to optimize the particle swarm algorithm function and optimize the learning rate and number of hidden layers of the neural networks. The FA-PSO-LSTM deep learning model used is an innovative and relatively new model that can fully benefit from the advantages of the LSTM network in time series processing and lead to the evolution of the theory in this regard. The results indicate that the proposed model predicts financial risk in the petroleum products industry with high accuracy and highlights the importance of variables such as liquidity, cash flow, and profitability. In the automotive industry, indicators such as liquidity, operational capacity, and sustainable development were more effective, while in the basic metals industry, the most data stability was observed and the best model performance was recorded. Overall, the variables of liquidity, profitability, cash flow, operational capacity, and development capability were identified as key common risk factors. It was also found that the proposed model has higher accuracy and efficiency compared to traditional methods and other algorithms..

کلیدواژه‌ها [English]

  • Financial Risk
  • Deep Learning
  • Publicly Traded Companies
  • Optimization

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پژوهش‌های راهبردی بودجه و مالیه
دوره 6، شماره 4 - شماره پیاپی 24
فصلنامه زمستان
بهمن 1404
صفحه 11-30

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سابقه مقاله

  • تاریخ دریافت: 21 تیر 1404
  • تاریخ بازنگری: 06 آبان 1404
  • تاریخ پذیرش: 20 بهمن 1404

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