Application of Machine Learning Methods (GPR-LASOO) To predict the Value of Firms Listed in Tehran Stock Exchange

The value of a firm is important for shareholders, investors, managers, creditors and other stakeholders in order to assess its future and to estimate the risk and return on investment and stock prices. Investors have always concerns about estimating the future value of firms and they use various financial instruments in this regard. The purpose of this research is to explain the value of a firm using performance, management system and audit committee variables and applying the Lars machine learning method, as well as to forecast the value of the firm using the Lasso linear and Gaussian process nonlinear methods to help decision makers and investors. For this purpose, data is collected from 208 years/firms listed in Tehran Stock Exchange during the seven financial period of 2011-2017. The preliminary results of the research show that performance criteria are more capable of explaining the value of a firm than the group of criteria of the management system and the audit committee. Also other results of the research indicate the high power of machine learning methods for forecasting the value of a firm, in particular, the Gaussian process nonlinear approach as compared to the linear method of the Lasso is.