Presenting financial bankruptcy risk prediction model of stock and transborder companies using machine learning algorithms
Subject Areas : Journal of Capital Market Analysis
Mohsen
Aali
1
(Ph.D. Student in Industrial Management, Islamic Azad University. Rudehen, Iran)
Seyed Alireza
Mirarab Baygi
2
(Faculty Member of Islamic Azad University. Rudehen. Iran)
Nima
Farajian
3
(Lecturer University of Kashan, Iran)
Keywords: Machine Learning, Random forest, Logistic regression, Financial Bankruptcy, Collective Learning,
Abstract :
Bankruptcy or business failure can have a negative impact on both the company itself and the global economy. In this research, the financial bankruptcy risk prediction of stock and transborder companies has been done using machine learning algorithms, Where the ultimate goal is to predict the financial bankruptcy risk of stock exchange and transborder companies. Collective learning is a field of machine learning in which instead of using a model to solve a problem, Use multiple models in combination to increase the output estimation power of the model. Each model is retrained using optimal features. As a result, the accuracy of predicting machine learning model by Stacking method, which is one of the strongest techniques of collective learning, To predict financial bankruptcy risk is higher than similar methods. Investors always want to prevent the deterioration of their capital by anticipating the possibility of a company's bankruptcy. Therefore, they are looking for ways to predict the bankruptcy of companies.
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