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قدرت شبکه عصبی پیچشی در پیشبینی درماندگی مالی | ||
| راهبرد مدیریت مالی | ||
| مقاله 4، دوره 11، شماره 2 - شماره پیاپی 41، تیر 1402، صفحه 77-96 اصل مقاله (473.46 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22051/jfm.2023.39916.2669 | ||
| نویسندگان | ||
| امین امینی مهر* 1؛ هانیه حکمت2 | ||
| 1دانشجوی دانشگاه ارشاد دماوند-واحد تهران | ||
| 2استادیار گروه حسابداری، دانشکده علوم اجتماعی و اقتصادی، دانشگاه الزهرا | ||
| چکیده | ||
| در این پژوهش ضمن نگاه بر سیر تکامل ادبیات پیشبینی درماندگی مالی، به ارائه یک مدل یادگیری عمیق پرداخته شده است. در این روش به شکلی مراحلی که روشهای پیشین برای پیشبینی درماندگی طی کردهاند، کوتاهتر و خودکارتر شده است. در نهایت، به مقایسه دقت پیشبینی مدل توسعه داده شده با مدلهای پیشین در این حوزه پرداخته شده است. در این پژوهش یک شبکه عصبی پیچشی بهعنوان یک مدل یادگیری عمیق که دادههای 14 متغیر مرتبط با پیشبینی درماندگی مالی را در طول 3 سال متوالی واکاوی میکند، برای پیشبینی درماندگی مالی مورداستفاده قرار گرفته است.بدر این راستا، بهمنظور جلوگیری از خطاهای احتمالی تعمیمپذیری، از روش K-fold برای نمونهگیری فرعی استفاده شده است که دادههای 300 نمونه را مورد بررسی قرار میدهد. در نهایت، با استفاده از آزمون ناپارامتریک Wilcoxon به بررسی معنیدار بودن اختلاف دقت پیشبینی ارائه شده میان مدل توسعه داده شده و مدلهای پیشین پرداخته شده است. نتایج این پژوهش نشان میدهد مدل شبکه عصبی پیچشی به شکل معنیداری در سطح اطمینان 95 درصد مدلهای پیشبینی درماندگی سابق از جمله رگرسیون لجستیک و ماشین بردار پشتیبان را در دقت پیشبینی شکست میدهد. | ||
| کلیدواژهها | ||
| درماندگی مالی؛ پیشبینی؛ شبکه عصبی پیچشی؛ یادگیری عمیق | ||
| عنوان مقاله [English] | ||
| The Strength of Convolutional Neural Network in Financial Distress Prediction | ||
| نویسندگان [English] | ||
| Amin Aminimehr1؛ Hanieh Hekmat2 | ||
| 1student of Ershad Damavand institute of higher education | ||
| 2Assistant professor of accounting, Alzahra University Tehran, Iran | ||
| چکیده [English] | ||
| Aim of study: In this study, by reviewing at the literature of financial distress prediction, a deep learning method has been developed that automates and shortens the earlier procedures of financial distress prediction. At last, the developed method is validated by comparing it to the earlier methods of this area. Methodology: The developed Convolutional Neural Network is used to extract knowledge within 14 related features to financial distress, through 3 consecutive years. In order to avoid the probable generalization error, the advantage of K-fold method to slice the entire 300 sample into sub-samples, was taken. Finally, the advantage of non-parametric Wilcoxon test was taken to verify the differences between the accuracy of the proposed method with the earlier ones. Result: Results of this study implies that the Convolutional Neural Network beats the other well-known methods in this area such as logistic regression and support vector machine with 95 percent confidence interval, in terms of prediction accuracy. | ||
| کلیدواژهها [English] | ||
| Financial distress, Prediction, Convolutional Neural Network, Deep Learning | ||
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