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مقایسهی دقت مدلهای آماری و یادگیری ماشین برای پیشبینی نگهداشت وجه نقد و ارائه مدل بهینه | ||
راهبرد مدیریت مالی | ||
مقاله 1، دوره 11، شماره 3 - شماره پیاپی 42، مهر 1402، صفحه 1-28 اصل مقاله (478.32 K) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22051/jfm.2023.42943.2789 | ||
نویسندگان | ||
سجاد میرزایی؛ مهدی محمدی؛ غلامرضا منصور فر* | ||
گروه حسابداری و مدیریت مالی، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران | ||
چکیده | ||
پژوهش حاضر، مقایسه دقت مدلهای یادگیری ماشین و آماری در پیشبینی نگهداشت وجه نقد را با استفاده از مجموعه متغیرهای مالی و اقتصادی مورد بررسی قرار داده است. روششناسی پژوهش را میتوان به سه مرحله گزینش مجموعه داده و متغیرها، مدلسازی و قیاس تقسیمبندی کرد. نمونهآماری پژوهش حاضر بورس اوراق بهادار تهران است که دادههای 173 شرکت در طی بازه زمانی 1400-1389 مورد بررسی قرارگرفته است. نتایج حاکی از دقت بالای مدل رگرسیون نمادین با استفاده از الگوریتم ژنتیک با ضریب دقت 71 درصد در این زمینه است. بعدازآن به ترتیب مدلهای تقویت گرادیان درختی، رگرسیون مارس، شبکه عصبی و تقویت گرادیان فوقالعاده بهعنوان دقیقترین مدلها جهت پیشبینی ارزیابی شدند. درنهایت مدل K نزدیکترین همسایه ضعیفترین دقت پیشبینی را از خود نشان داد. همچنین اگرچه مدلهای آماری دقت پیشبینی پایینی را نشان دادند اما بااینحال از برخی مدلهای یادگیری ماشین ضریب دقت بالاتری را کسب کردند. همچنین نتایج نشان داد استفاده از رگرسیون لاسو موجب بهبود دقت مدلهای آماری و برخی از مدلهای یادگیری ماشین میگردد. این پژوهش میتواند زوایای جدیدی از تکنیکهای پیشبینی نگهداشت وجه نقد را در مطالعات مالی بیفزاید؛که تاکنون در ادبیات مالی مورد بررسی قرار نگرفته است. | ||
کلیدواژهها | ||
رگرسیون لاسو؛ پیشبینی نگهداشت وجه نقد؛ یادگیری ماشین | ||
عنوان مقاله [English] | ||
Comparison of Statistical and Machine Models for Predicting Cash Holdings and Providing the Optimal Model | ||
نویسندگان [English] | ||
Sajjad Mirzaei؛ Mehdi Mohammadi؛ Gholamreza Mansourfar | ||
Accounting and Finance Dept., Faculty of Economics and Management, Urmia University, Urmia, Iran. | ||
چکیده [English] | ||
The current paper has investigated the comparison of the accuracy of machine learning and statistical models in predicting cash holdings using a set of financial and economic variables. Research methodology can be divided into three stages: selection of data set and variables, modeling and estimation. The statistical sample of the current research is the Tehran Stock Exchange, where the data of 173 companies have been analyzed during the period of 2010-2021. The results indicate the high accuracy of the symbolic regression model using the genetic algorithm with an accuracy factor of 71% in this field. After that, Gradient Boosted Trees, MARS regression, neural network and XGboost models were evaluated as the most accurate models for prediction. Finally, the KNN model showed the weakest prediction accuracy. Also, although the statistical models showed low prediction accuracy, they obtained a higher accuracy coefficient from some machine learning models. Also, the results showed that the use of Lasso regression improves the accuracy of statistical models and some machine learning models. This research can add new angles of cash retention forecasting techniques in financial studies, which have not been investigated in financial literature so far. | ||
کلیدواژهها [English] | ||
Lasso Regression, Machine Learning, Predict Cash Holdings | ||
سایر فایل های مرتبط با مقاله
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