The Role of Performance and Governance Criteria in Determining the Price of Shares with an Artificial Intelligence-based Approach

Abstract

Objective: The purpose of this research is to explain the price of share using performance variables, management system and audit committee, as well as forecasting the price of share to help decision makers and investors. Methods: For this purpose, the information about 208 listed companies in Tehran Stock Exchange, during 2010-2017, have been extracted and analyzed using the linear and nonlinear method of Gaussian process. Results: Preliminary research results show that performance criteria are more capable of explaining stock prices compared to governance criteria. The results also indicate the high power of machine learning methods for predicting the price of companies' stocks. The latter is especially true for the nonlinear Gaussian process, which has also proven to perform better than the CART or the lasso algorithms. Conclusion: Since corporate governance and the audit committee are two governing bodies of the stock exchange in Iran, and the stock market participants are not aware of how they operate, it seems that the performance information content is better for investors than governance measurements. Among the non-financial criteria, ownership concentration is the only factor that is able to explain the price of share of the company, and it can be argued that major shareholders have the motivation and ability to supervise the manager and increase the company's performance.

Keywords

• company value
• performance criteria
• corporate governance criteria
• machine learning method