Application of methods radial neural network, Gaussian process regression in predicting financial constraints Companies admitted to the Tehran Stock Exchange
Subject Areas : Financial engineering
Mohammadreza
Gholamzadeh
1
(PhD student of Accounting, Faculty of Humanities, Zahedan Branch, Islamic Azad University, Zahedan, Iran.)
Mahdi
faghani
2
(Department of Accounting, Faculty of Management and Economics, , University of Sistan and Baluchestan, Sistan and Baluchestan,iran.)
ahmad
pife
3
(Department of Accounting, Faculty of Management and Accounting, University of Sistan and Baluchestan, Sistan and Baluchestan, Iran.)
Keywords: financial constraints, radial neural network, Gaussian process regression,
Abstract :
One of the important issues in predicting financial constraints is the choice of predictor variables. In this study, we investigated the Gaussian process machine learning method and radial neural network to predict financial constraints. For this purpose, 208 companies from 1390 to 1396 have been selected as the statistical population. Due to the availability of information, all companies have been considered as a statistical sample. The results of this study showed that machine learning methods have the ability to predict the financial constraints of corporations admitted to Tehran Stock Exchange. Therefore, the main hypothesis of this research is confirmed and machine learning methods are an effective way to predict financial constraints. Also, the results showed that the company's value, operating cash flow ratio, financial leverage, return on assets, and the percentage of institutional owners had the most importance in predicting financial constraints.
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