Comparison of multiple linear regression and machine learning algorithms inPredicting cash holdings
Subject Areas : Financial engineering
samira seif
1
(Assistant Professor, Department of Accounting, Payame Noor University, Tehran, Iran.)
mostafa yousofi tezerjan
2
(Assistant Professor, Department of Industrial Engineering, University of Applied Science and Technology (UAST), Karaj, Iran)
Keywords: forecasting, multiple linear regression, Cash holdings, Machine Learning Algorithms,
Abstract :
In recent years, in the financial literature, more attention has been paid to the level of cash holding of companies. So; Forecasting is important to determine the optimal level of cash holding. In this research, using linear and non-linear methods and 13 influential input variables, the amount of cash in 103 companies admitted to the Iran Stock Exchange during the years 2013 to 2021 has been predicted. The methods used include multiple linear regression (MLR), k nearest neighbor (KNN), support vector machine (SVM) and multi-layer neural networks (MLNN) for prediction. The results show that the traditional method of multiple linear regression has not been successful in predicting cash, but machine learning algorithms have been superior with an accuracy of 0.99. The variables of profit per share, the ratio of current assets to current liabilities and the ratio of short-term debt to total assets have had a greater impact in all algorithms. Therefore, managers can use advanced machine learning algorithms to predict the future cash flow of companies.
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