A Machine Learning Approach for Selecting Directors of Chinese Listed Company

Abstract

Contemporarily, the directors sever as a vital role in companies strategy decisions and daily operation. On this basis, it is crucial to select a suitable and appropriate directors of listed companies to fulfill the requirements and criteria of shareholders (at least most of them). In this case, this paper discusses a financial big data-based machine learning approach for board selection of Chinese listed companies. This approach achieves effective feature extraction through big data related to changes in boards of directors and successfully identifies the outstanding features of excellent boards. The empirical results show that machine learning models have a significant advantage over traditional methods, such as ordinary least squares (OLS) regression, for board selection by Chinese companies. According to the analysis, the model can extract important features of an excellent board of directors from big data related to the board’s features, such as academic, research and development (R&D), and politician backgrounds. These results shed light on guiding further exploration of board selection in China.