مروری بر نظریه ها، مدل ها و تکنیک های پیش بینی درماندگی مالی و ورشکستگی شرکت ها (مقاله پژوهشی دانشگاه آزاد)
درجه علمی: علمی-پژوهشی (دانشگاه آزاد)
آرشیو
چکیده
اهداف: با توجه به پیامدهای نامطلوب اقتصادی و اجتماعی ورشکستگی شرکت ها و اهمیت پیش بینی به موقع درماندگی مالی آنها برای انجام اقدامات اصلاحی، نگارنده در این پژوهش به بررسی پژوهش های مرتبط با مدل های پیش بینی درماندگی مالی و ورشکستگی در ایران و سایر کشورها می پردازد.روش: این مقاله پژوهشی مروری است که با بررسی 102 پژوهش در ایران و 298 پژوهش در سایر کشورها طی سال های 2023-1930، به تشریح نظریه ها و و تحلیل ماهیت، روند، ترکیب و گستره زمانی استفاده از مدل ها و تکنیک های پیش بینی درماندگی مالی و ورشکستگی می پردازد و پیشنهادهای اجرایی و پژوهشی نوینی را ارائه می کند.نتایج: نتایج پژوهش نشانگر روند افزایشی این پژوهش ها در ایران و روند کاهشی آن در سایر کشورها است. ترکیب مدل ها و تکنیک های به کاررفته در سایر کشورها متنوع تر از ایران است و مدل ها و تکنیک های هوشمند مبتنی بر محاسبات تکاملی به متداول ترین و دقیق ترین تکنیک ها تبدیل شده است. پیشنهادها نیز شامل تنوع بخشیدن به تکنیک های پیش بینی، تأکید بر مدل های مبتنی بر الگوریتم های محاسبات تکاملی، توجه به ضرورت پیش بینی های مجدد پس از بحران، توسعه مدل های ویژه برای شرکت های کوچک و متوسط، تفکیک معیارهای تشخیص درماندگی مالی و ورشکستگی در بورس تهران، انجام پیش بینی های پویا و توسعه مدل های تخصصی تر برای افزایش دقت پیش بینی ها است.نوآوری: نوآوری این پژوهش شامل ارائه بینش تحلیلی پویا، اطلاعاتی به روز تر و جامع تر، رفع نقایص منسوخ بودن و محدودیت موضوعی و زمانی پژوهش های قبلی، پوشش بهتر و بررسی جداگانه پژوهش های ایرانیان و مقایسه این پژوهش ها در ایران با سایر کشورها و ارائه توصیه هایی کاربردی و پژوهشی جدید براساسِ نتایج تحلیل جامع است.A Review of Theories, Models, and Techniques for Predicting Corporate Financial Distress and Bankruptcy
Due to the high costs of financial distress and bankruptcy and the importance of timely prediction of financial distress to take corrective actions, this study examines the research related to the prediction models of financial distress and bankruptcy in Iran and other countries. This study is a review of research that examines 102 research in Iran and 298 research in other countries during the years 1930-2023, describing theories and analyzing the nature, trend, composition, and periods of using models and presenting practical and research recommendations. The results indicate an upward trend of research in Iran, while it is declining in other countries. The noteworthy point is the use of various combinations of models and techniques in other countries is more diverse than in Iran, and the prominence of intelligent models that are based on evolutionary calculations. The research concludes by providing executive and research recommendations. The recommendations include diversifying predicting techniques, emphasizing models based on evolutionary computing algorithms, paying attention to the predictions after the crisis, developing special models for small and medium-sized companies, separating the criteria for recognizing financial distress and bankruptcy in the Tehran Stock Exchange, making dynamic predictions, and developing more focused and specialized models to increase accuracy. This study addresses the shortcomings of past research, provides more up-to-date information and insights on the dynamic predictions, compares these studies in Iran to other countries, and provides some executive recommendations and research topics based on the results.Keywords: Financial Distress, Bankruptcy, Model and Technique, Theories, Prediction. IntroductionFinancial distress indicates that a company is approaching bankruptcy and the vast and profound negative effects of bankruptcy on society, necessitate reviewing bankruptcy prediction models. Therefore, predicting financial distress in its early stages can inform the stakeholders of companies about their future possible losses (Zhou et al, 2023). Global crises like the 2008 financial crisis and the COVID-19 pandemic have forced even very strong international companies to continuously monitor their financial situation (Woodlock & Dangol, 2014; Hassan, 2022; Papik & Papikova, 2023). These environmental dynamics require reforms and more accurate predicting methods for the financial health of enterprises, (Brygala, 2022) especially for small and medium-sized companies, which have weaker financial resources (Ciampi et al. 2021; Mirza et al., 2023). While existing literature reviews are often outdated or limited in scope, this study addresses these gaps by comprehensively analyzing the models and techniques used for predicting financial distress and bankruptcy in Iran and other countries from 1930 to 2023. Furthermore, the study compares the nature, trends, composition, and periods of use of predicting models in Iran and other countries and offers insights for future research. Materials and MethodsSystematic review refers to examining, criticizing, and evaluating a specific research topic, extracting and interpreting data from published articles, and analyzing, and describing results based on clear evidence (Lasserson et al., 2020). It generates highly credible, low-bias, and quality scientific research documents (Yetley et al., 2016). This meta-analysis research systematically evaluates financial distress and bankruptcy prediction models by analyzing 102 studies from Iran and 298 from other countries from 1932 to 2023. Data were sourced from reputable Iranian and international databases and journals, including Emerald, Science Direct, and ProQuest. The study aims to rectify previous shortcomings, update information, and offer new research avenues based on the findings of this review.Research FindingsIn Iran, financial distress prediction studies that started in 2001 shows a rising trend, encompassing 13 studies conducted during 1968-2007 and 89 studies during 2007-2023. Financial distress prediction research in other countries started in 1932 and has shown a declining trend in recent years including 10 studies conducted during 1932-1968, 217 studies in 1968-2007, and 71 studies during 2007-2023. The 102 predicting techniques in Iran during 2001-2023 consisted of 43 statistical models, 40 intelligent models, 17 combined models, and 2 other models such as Data Envelopment Analysis and judgmental, etc. However, 298 studies in other countries include 117 statistical models, 125 intelligent models, 25 combined models, 21 theoretical models, and 10 other models such as Data Envelopment Analysis and judgmental, etc. Among the 102 papers from Iran, 61.76% introduced modified or new models, while 87.5% of global research presented innovative models. Techniques used globally from 1930 to 2023 include statistical, intelligent, hybrid, judgmental, and other models. Statistical models dominated earlier, however, intelligent models, particularly those inspired by animal collective intelligence, gained prominence and attention for their high accuracy. Discussion and ConclusionsIranian research on predicting financial distress and bankruptcy exhibits an upward trend, while global research has been declining recently. The results verify that intelligent models like neural networks and genetic algorithms, notably those inspired by animal collective intelligence such as the ant algorithm, and the firefly algorithm, the bird algorithm, demonstrated higher prediction accuracy than statistical models such as multiple discriminant analysis, logistic regression, probit, and theoretical models. Successful intelligent models, which widely used recently and exhibited a higher accuracy, include gradient boosting models (Jones, 2017), machine learning methods (Chen et al., 2023), and models based on collective intelligence such as the firefly algorithm (Bayat et al., 1997). Among the statistical techniques, the nonlinear logistic regression techniques demonstrated a high level of accuracy (Lohmann et al., 2022; Lohmann & Mollenhoff, 2023). In addition, 61.76% of 102 models from Iran and 87.5% of 298 articles abroad were innovative models. Recommendations include diversifying predicting techniques, emphasizing models that are based on evolutionary computing algorithms, attention to the necessity of post-crisis predictions, developing tailoring models for SMEs, separating criteria for financial distress and bankruptcy recognition in the Tehran Stock Exchange, the necessity of adopting dynamic predictions and (Lohmann & Molenoff, 2023) developing focused and industry-focused models for enhanced accuracy (Nazmi Ardakani et al., 2017).
تبلیغات
