تاثیر اطلاعات سرمایه در گردش در پیش‌بینی درماندگی مالی بر مبنای ترکیب شبکه‌ عصبی مصنوعی و الگوریتم تجمعی حرکت ذرات

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه حسابداری، واحد بافت، دانشگاه آزاد اسلامی، بافت، ایران

2 دانشجو دکتری حسابداری، دانشگاه شیراز، شیراز، ایران.

چکیده

با وجود این واقعیت که مدیریت سرمایه در گردش در درماندگی مالی نقشی محوری دارد؛ اما مطالعات انجام‌شده در این زمینه، تاکنون درک عمیقی‌ از چگونگی تأثیرگذاری اطلاعات سرمایه در گردش بر درماندگی مالی را فراهم نکرده‌ است؛ از این رو هدف اصلی این پژوهش بررسی تاثیر اطلاعات سرمایه در گردش در پیش‌بینی درماندگی مالی بر مبنای ترکیب شبکه‌ عصبی مصنوعی و الگوریتم بهینه‌سازی حرکت تجمعی ذرات است. نمونۀ آماری متشکل از 120 شرکت پذیرفته‌شده در بورس اوراق بهادار تهران برای دورۀ زمانی 1387 تا ۱۳۹۸ است. در راستای دستیابی به اهداف پژوهش، ابتدا 28 متغیر اثرگذار بر درماندگی مالی انتخاب و سپس با استفاده از روش رگرسیون لجستیک پیش‌رو مدل برآورد و 5 متغیر تاثیرگذار انتخاب گردید. در گام بعد، به منظور بررسی محتوای اطلاعاتی مدیریت سرمایه در گردش به مقایسه مدل پژوهش با توجه و بدون توجه به متغیر مدیریت سرمایه در گردش بر مبنای ترکیب شبکه‌های عصبی مصنوعی و الگوریتم بهینه‌سازی حرکت تجمعی ذرات پرداخته شده‌است. نتایج مقایسه دو مدل نشان داد توسعه مدل پژوهش، خطای آموزش شبکه عصبی با الگوریتم حرکت تجمعی ذرات را بـه مقـدار 0641/0 کـاهش می‌دهد. همچنین، با توسعه مدل پژوهش، از طریق وارد کردن متغیر مدیریت سرمایه در گردش، سطح زیرمنحنی راک به 6248/0 افزایش می‌یابد و در نتیجه، بر دقت مدل پژوهش تا 53/70 درصد افزوده می‌شود. همچنین نتایج، افزایش قدرت مدل توسعه یافته پژوهش را نشـان مـی‌دهـد؛ امـا نتیجه آزمون ضعیف است و نشان می‌دهد مدل توسعه‌یافته پژوهش نیز در تفکیک شـرکت‌هـا به دو گـروه درمانده و غیر درمانده مالی، کمابیش یک مدل تصادفی است

کلیدواژه‌ها

عنوان مقاله [English]

The Effect of Working Capital Information in Predicting Financial Distress Based on Combination of Artificial Neural Network and Particle Swarm Optimization Algorithm

نویسندگان [English]

  • Sedighe Azizi 1
  • Hossein Jokar 2
1 Assistant Prof., Department of Accounting, Baft Branch, Islamic Azad University, Baft, Iran
2 Ph.D. Candidate in Accounting, Shiraz University, Shiraz, Iran.
چکیده [English]

The purpose of this research is to investigate the information of circulation capital management information for predicting financial helplessness based on artificial neural networks and particle cumulative optimization algorithms. The statistical population of the research consists of 120 companies listed in Tehran Stock Exchange during the years 2008-2019. In order to achieve the goals of the research, first, by studying previous studies in the field of financial distress, 28 variables affecting financial distress and then, using the leading logistic regression model, the estimated model and 5 variables were selected. Then, in order to verify the information of the information management information in circulation, comparing the research model with attention and regardless of the circulation of circulation management based on the combination of artificial neural networks and the optimization of the cumulative particle movement. The results of the two models based on the combination of artificial neural networks and the optimization algorithm of cumulative particle movement showed that the development of the research model reduced the error of neural network training with the cumulative particle movement algorithm to 0.0641. Also, with the development of the research model, the subcutaneous level of Rock increases to 6,248 and, consequently, the research model is added to 70.5%. This result shows the effectiveness of the entry of capital management in the research model.

کلیدواژه‌ها [English]

  • Working Capital
  • Financial Distress
  • Particle Swarm Optimization Algorithm

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چشم انداز مدیریت مالی
دوره 12، شماره 38
تیر 1401
صفحه 75-101

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