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Identification of the Most Critical Factors in Bankruptcy Prediction and Credit Classification of Companies | ||
| Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
| دوره 14، شماره 4، دی 2021، صفحه 817-834 اصل مقاله (866.33 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijms.2021.285398.673712 | ||
| نویسندگان | ||
| Gholamreza Jandaghi1؛ Alireza Saranj* 2؛ Reza Rajaei3؛ Ahmadreza Ghasemi4؛ Reza Tehrani5 | ||
| 1Professor, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran | ||
| 2Assistant Professor, Department of Finance and Accounting, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran | ||
| 3PhD in Financial Management, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran | ||
| 4Assistant Professor, Department of Industrial Management, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran | ||
| 5Professor, Department of Financial Management, Faculty of Management, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| Banks and financial institutions strive to develop and improve their credit risk evaluation methods to reduce financial loss resulting from borrowers’ financial default. Although in previous studies, many variables obtained from financial statements – such as financial ratios – have been used as the input to the bankruptcy prediction process, seldom a machine learning method based on computing intelligence has been applied to select the most critical of them. In this research, the data from companies that are were listed in Tehran’s Stock Exchange and OTC market during 26 years since 1992 to 2017 has been investigated, with 218 companies selected as the study sample. The ant colony optimization algorithm with k-nearest neighbor has been used to feature the selection and classification of the companies. In this study, the problem of the imbalanced dataset has been solved with the under-sampling technique. The results have shown that variables such as EBIT to total sales, equity ratio, current ratio, cash ratio, and debt ratio are the most effective factors in predicting the health status of companies. The accuracy of final research model is estimated that the bankruptcy prediction ranges between 75.5% to 78.7% for the training and testing sample. | ||
| کلیدواژه‌ها | ||
| Credit risk؛ Probability of default (PD)؛ Bankruptcy prediction (BP)؛ K-nearest neighbor (KNN)؛ Ant colony algorithm؛ Imbalanced dataset | ||
| عنوان مقاله [English] | ||
| شناسایی مهمترین عوامل در پیشبینی ورشکستگی و طبقهبندی اعتباری شرکتها | ||
| نویسندگان [English] | ||
| غلامرضا جندقی1؛ علیرضا سارنج2؛ رضا رجایی3؛ احمدرضا قاسمی4؛ رضا تهرانی5 | ||
| 1استاد، دانشکده مدیریت و حسابداری، پردیس فارابی، دانشگاه تهران، قم، ایران | ||
| 2استادیار، گروه مدیریت مالی و حسابداری، دانشکده مدیریت و حسابداری، پردیس فارابی، دانشگاه تهران، قم، ایران | ||
| 3دکترای مدیریت مالی، دانشکده مدیریت و حسابداری، پردیس فارابی، دانشگاه تهران، قم، ایران | ||
| 4استادیار، گروه مدیریت مالی و حسابداری، دانشکده مدیریت و حسابداری، پردیس فارابی، دانشگاه تهران، قم، ایران | ||
| 5استاد، گروه مدیریت مالی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران | ||
| چکیده [English] | ||
| بانک ها و موسسات مالی کوشش می کنند که روشهای ارزیابی ریسک اعتبارییشان را به منظور کاهش زیان مالی ناشی از نکول مالی قرضگیرندگان، توسعه داده و بهبود بخشند. هر چند در مطالعات گذشته، تعداد زیادی از متغیرهای مستخرج از صورتهای مالی شامل نسبتهای مالی به عنوان ورودی فرایند پیشبینی ورشکستگی مورد استفاده قرار گرفته شده بود، کمتر یک روش یادگیری ماشینی که بر اساس هوش محاسباتی باشد، در انتخاب کلیدیترین متغیرها به کار گرفته شده بود. در پژوهش حاضر، دادههای شرکتهای حاضر در بازار بورس تهران و فرابورس در طول 26 سال از 1992 تا 2017 به عنوان جامعه پژوهش مورد بررسی قرار گرفت و 218 شرکت به عنوان نمونه انتخاب شد و الگویتم کلونی مورچگان به همراه الگوریتم نزیکترین k همسایگی به منظور انتخاب ویژگی و طبقهبندی شرکتها مورد استفاده قرار گرفت. در این پژوهش مساله نامتقارن بودن مجموعه دادهها با تکنیک زیرنمونهبرداری حل شده است. نتایج نشان میدهد متغیرهایی از قبیل نسبت EBIT به فروش کل، مالکانه، جاری، وجه نقد و بدهی، موثرترین عوامل در پیشبینی وضعیت سلامت اعتباری شرکتها هستند. مدل نهایی پژوهش قادر به تخمین احتمال ورشکستگی با دقت بین 75.5 تا 78.7 درصد برای نمونه آموزش و آزمون میباشد. | ||
| کلیدواژه‌ها [English] | ||
| ریسک اعتباری, احتمال نکول, پیشبینی ورشکستگی, نزدیک‌ترین k همسایگی, الگوریتم کلونی مورچگان, دادههای نامتقارن | ||
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