Forecast the operating cash flow of accepted companies In Tehran Stock Exchange using machine learning method

Cash is the fluid financial assets of companies. This feature of cash flow has given it tremendous importance, and the ability to make optimal and timely financial decisions is greatly influenced by this feature. Companies with good domestic cash flows are less likely to rely on external financing, and lenders can easily lend to these companies because of their good liquidity. The present study is an applied research in terms of purpose. Also, in this study, the combined data method has been used. Data collection method, document mining method and referring to databases; And the method of data analysis is inferential. In the present study, the required data have been extracted from the new Rahvard software, corporate financial statements and syndication, as well as the Codal site. The statistical population of the present study is all companies listed on the Tehran Stock Exchange in the period2011 to 2018and the financial information of 138 companies has been used over 8 years. The purpose of this study is to predict operational cash with PLSVM and CART artificial intelligence approach in companies listed on the Tehran Stock Exchange. In this study, the company's operating cash ratio was considered as a dependent variable (liquidity) and financial metrics were considered as the initial independent variable. The results of testing the research hypotheses show that the parametric nonlinear law-based artificial intelligence approach has a high ability to predict the liquidity of companies on the Tehran Stock Exchange.