Investment Value Evaluation of Listed Companies Based on Machine Learning

In this paper, we investigate a heterogeneous set of listed companies and extract significant features that can accurately evaluate the investment value of them. Specifically, we analyze both financial data and non-financial data, including: corporate annual report, commercial information, industrial information, land acquisition information, financial information, tax report, intellectual property report, etc. In order to effectively handle a large number of categorical features and mitigate over-fitting problem, CatBoost, LightGBM and ensemble learning framework are adopted to output precise value for each enterprise. Furthermore, extensive experiments demonstrate that the proposed framework achieves state-of-the-art result with RMSE as low as 2.97. Finally, we find that in addition to financial features, many non-financial factors such as Number of Patents (NOP) and Number of Qualification Certifications (NOQC) also play important roles in company investment value evaluation.