Company value prediction based on deep learning methods

Prediction and clear understanding of the behavior of a phenomenon plays a major role in adopting strategies and decisions. All-round development and deepening of the capital market as the driving engine of economic development requires the public trust of participants in its efficiency and correctness in determining the fair price of securities. On the other hand, predicting company value, price fluctuations, or stock returns is very important in portfolio selection, asset management, and even stock pricing of newly listed companies.In this research, using the data of 159 companies during a 10-year period including 2011-2020 and the factors affecting the company's value, including financial ratios, corporate governance mechanisms, macroeconomic factors, and the stock market, the company's value has been predicted. In this research, two structures of deep learning methods including GRU and BLSTM are used for better evaluation. The results of examining the data collected using deep learning techniques indicated that the combined model with a lower RMSE error than the GRU model predicted the value of the company.