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پیش‌بینی بحران مالی در بانک‌ها با استفاده از مدل‌های معادلات ساختاری | ||
| راهبرد مدیریت مالی | ||
| مقاله 1، دوره 11، شماره 2 - شماره پیاپی 41، تیر 1402، صفحه 1-28 اصل مقاله (654.11 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22051/jfm.2023.42910.2788 | ||
| نویسندگان | ||
| محمد سلیمانی* 1؛ محمد عرب‌مازار یزدی2؛ جواد شکرخواه3؛ محمدحسین صفرزاده4 | ||
| 1دانشجوی دکتری، گروه حسابداری، دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران. | ||
| 2دانشیار، گروه حسابداری، دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران | ||
| 3دانشیار، گروه حسابداری، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبایی، تهران، ایران | ||
| 4استادیار، گروه حسابداری، دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران | ||
| چکیده | ||
| هدف از انجام این پژوهش، ارائه مدلی یکپارچه جهت پیش‌بینی وقوع بحران مالی در بانک‌ها است. برای انجام این‌کار، ابتدا ادبیات نظری موجود حول موضوع پژوهش بررسی شده و عوامل اثرگذار بر بحران مالی در بانک‌ها شناسایی شده است. در ادامه داده‌های پژوهش برای 21 بانک نمونه از سال 1391 تا سال 1400، جمع‌آوری و تجزیه و تحلیل شده و مدل پیش‌بینی بحران مالی در بانک‌ها با استفاده از روش معادلات ساختاری، مدل‌سازی شده است. در نهایت، برای بررسی میزان دقت مدل طراحی‌شده، از روش شبکه‌های عصبی مصنوعی استفاده شده است. نتایج حاصل از پژوهش نشان داد که عوامل مالی، عوامل درونی غیرمالی، عوامل صنعت و عوامل کلان بر بحران مالی در بانک‌ها موثر هستند و متغیرهای منابع انسانی، حاکمیت شرکتی، ریسک نقدینگی و نسبت کفایت سرمایه مهم‌ترین متغیرها در پیش‌بینی بحران مالی هستند. همچنین، این مدل توانسته است بانک‌های دارای بحران و بانک‌های سالم را با دقت 100 درصد در هر دو گروه آموزش و آزمایش به درستی پیش‌بینی نماید. این مدل می‌تواند با معرفی عوامل موثر بر بحران، فرصت مناسب جهت انجام اقدامات پیشگیرانه و اصلاحی را در اختیار مدیران بانک‌ها و همچنین بانک مرکزی قرار دهد. | ||
| کلیدواژه‌ها | ||
| بحران مالی بانکی؛ مدل پیش‌بینی بحران؛ مدل معادلات ساختاری؛ شبکه‌های عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Anticipation of Financial Crisis in Banks by Structural Equations Model | ||
| نویسندگان [English] | ||
| Mohammad Soleymani1؛ Mohammad ArabMazar Yazdi2؛ Javad Shekarkhah3؛ Mohammad Hoseyn Safarzadeh4 | ||
| 1PhD student, Department of Accounting, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran. | ||
| 2Associate Professor, Management and Accounting faculity, Shahid Beheshti University, Tehran, IRAN | ||
| 3Associate Professor, Department of Accounting, Faculty of Management and Accounting, Allameh Tabatabai University, Tehran, Iran | ||
| 4Assistant Professor, Department of Accounting, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran | ||
| چکیده [English] | ||
| Undeniable effects of banking crisis on economy and need for anticipating the crisis before occurrence, remind necessity of existence of a model for anticipating the banking crisis. The aim of this study is to provide a complete model that includes all of the effective variables on the banking crisis, so we can help better anticipation of banking crisis. For doing this research, the literature was studied and variables that cause banking crisis was identified. Next, research data for 21 banks from 2013 to 2022 was gathered and analyized. The research model was developed by structural equations modeling method. Finally for measuring the models precision, artificial neural networks was employed. The result of the research shows that financial variables, Internal non-financial variables, banking sector variables and macroeconomic variables cause bank distress and most important factors in bank distress anticipation are: human resource, corporate governance, liquidity and capital adequacy. Also, this model could distinguish between distressed bank and healthy one in both train and test groups with precision of 100 percent. With introducing the important factors causing the banking distress, this model can provide a buffer for bank managers and central bank for doing the preventive and corrective actions. | ||
| کلیدواژه‌ها [English] | ||
| Financial Crisis in Banks, Crisis Anticipation Model, Structural Equations Model, Artificial Neural Networks | ||
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