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مقایسه کارایی مدلهای یادگیری ماشین و مدل های آماری در پیش بینی ریسک مالی | ||
راهبرد مدیریت مالی | ||
مقاله 3، دوره 11، شماره 1 - شماره پیاپی 40، فروردین 1402، صفحه 53-76 اصل مقاله (524.45 K) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22051/jfm.2023.35240.2512 | ||
نویسندگان | ||
سامان توکلی1؛ علی آشتاب* 2 | ||
1گروه حسابداری، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران | ||
2استادیار گروه حسابداری، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران | ||
چکیده | ||
هدف این پژوهش، مقایسه کارایی مدلهای یادگیری ماشین (32 مدل) و مدلهای آماری (14 مدل)، در پیشبینی ریسک مالی 145 شرکت پذیرفته شده در بورس اوراق بهادار تهران طی بازه زمانی 1389 تا 1398 و انتخاب بهترین مدل با استفاده از تکنیکهای بهینهسازی پیشرفته میباشد. یافتههای پژوهش با استفاده از آزمون مقایسه دقت ضرایب پیشبینی، بیانگر آن است که با اطمینان 99 درصد، دقت پیشبینی مدلهای یادگیری ماشین، بیشتر از مدلهای آماری است. همچنین بهترین مدل یادگیری ماشین پس از بهینهسازی، مدل ماشین بردار پشتیبان تکاملی با دقت پیشبینی 99.86درصد و مقدار سطح زیر منحنی برابر0.998بوده است. علاوه بر این، نسبتهای مالی تعهدی با دقت پیشبینی99.45درصد و نسبتهای مالی فعالیت با دقت پیشبینی 98.62درصد توانستند در مقایسه با سایر نسبتهای مالی در استفاده از ماشین بردار پشتیبان تکاملی به منظور پیشبینی ریسک مالی عملکرد بهتری داشته باشند. از سوی دیگر ریسک مالی پیشبینی شده بر اساس صنایع مختلف، متفاوت بوده است. بنابراین مشخص شد که مدلهای یادگیری ماشین به دلیل عدم برخورداری از محدودیتهایی که مدلهای آماری با آن مواجهه هستند میتوانند به عنوان ابزاری مهم، در پیشبینی ریسک مالی شرکتها به کار روند. | ||
کلیدواژهها | ||
پیشبینی؛ ریسک مالی؛ ماشین بردار پشتیبان تکاملی؛ یادگیری ماشین | ||
عنوان مقاله [English] | ||
Comparison of the Effectiveness of Machine Learning Models and Statistical Models in Predicting Financial Risk | ||
نویسندگان [English] | ||
Saman Tavakoli1؛ Ali Ashtab2 | ||
1Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran | ||
2Assistant Professor, Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran | ||
چکیده [English] | ||
The purpose of this study was to compare the efficiency of machine learning models (32 models) and statistical models (14 models) in predicting the financial risk of listed 145 companies in Tehran Stock Exchange during the period 2010 to 2020 and selecting the best model using advanced optimization techniques. Findings of the research using the test of comparing the accuracy of prediction coefficients, indicates that with 99 percent confidence, the prediction accuracy of machine learning models is higher than statistical models. Also, the best machine learning model after optimization was the evolutionary support vector machine model with 99.86 percent prediction accuracy and the value of the area under the curve was 0.998. In addition, accrual financial ratios with 99.45 percent predictive accuracy and operating financial ratios with 98.62 percent predictive accuracy were able to perform better than other financial ratios in using the evolutionary support vector to predict financial risk. on the other side, the projected financial risk varied according to different industries. Therefore, it was found that machine learning models can be used as an important tool in predicting corporate financial risk due to the lack of limitations that statistical models face. | ||
کلیدواژهها [English] | ||
Financial Risk, Machine Learning, Prediction, Support Vector Machine Evolutionary | ||
سایر فایل های مرتبط با مقاله
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