The Modeling the Fixed Asset Investing with a Machine Learning Approach by Emphasizing the Role of Financial Criteria
الموضوعات :Farzaneh SHamsdoost 1 , Omid Mahmoudi Khoshro 2 , Ataollah Mohammadi Malgharni 3 , Amir Sheikhahmadi 4
1 - Department of Accounting , Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
2 - Department of Accounting , Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
3 - Department of Accounting , Sanandaj Branch, Islamic Azad University, Sanandaj, Iranandaj, Iran
4 - Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
الکلمات المفتاحية: Fixed Asset Growth , Financial Benchmark , Artificial Intelligence , Linear and non-linear Models,
ملخص المقالة :
The purpose of this research is to provide a growth model of fixed assets based on the financial criteria of companies admitted to the Tehran Stock Exchange. The current research is applied in terms of objective classification and descriptive-correlation in terms of method. The research method is de-ductive-inductive. The statistical population of the current research is all the companies admitted to the Tehran Stock Exchange in the period from 2012-2021 and the financial information of 101 companies are use. Research hypotheses were tested using artificial intelligence algorithm. In this research, investment in fixed assets has been consider as a dependent variable, and financial criteria has been considered as primary independent variables. The results of research hypotheses testing using the methods of linear and non-linear algorithms of artificial intelligence PINSVR and KPLSR in predicting fixed asset investors of companies and by calculating the three errors criteria MAE, MSE and SMAPE in annual fixed assets. The asset forecasting in the next year of companies showed that the error difference between linear models and non-linear models is not so great that it can be claim that linear models are ineffective in predicting asset growth so that artificial intelligence algorithms are capable of predicting investment in company assets.
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