Estimating Market Value of Companies Based on Finance Statement through Data Fusion

The evaluation of a company's value can serve as a guide for investors to assess the company and make informed investment decisions. However, conventional valuation techniques are not applicable to Initial Public Offering (IPO) companies in China, mainly due to the absence of historical market performance. In contrast, a company's finance statement provides a periodic overview of the company's operational and production activities, which is linked to its market performance. Traditional methods often rely on the selection of a limited number of financial indicators from the finance statement and the application of regression analysis. These approaches fail to fully exploit the comprehensive data available in the finance statement. This study proposes a comprehensive method that leverages all relevant information contained in the finance statement, including industry interconnections, financial indices, and additional insights obtained from the report. The structured data is analyzed through tree models, while the interrelationships between different companies are modeled through graph neural networks. Our approach offers a multi-perspective evaluation of IPO companies. The results of our experiments demonstrate that our method can effectively utilize the valuable information in finance statements and improve outcomes.