[1H5-OS-8c] OS-8

[1H5-OS-8c-02] Director Skill Matrix Estimation and Its Relationship with Corporate Characteristics

〇Kaito Takano¹, Dai Yamawaki^1,2, Koutarou Tamura³, Kei Nakagawa^1,4 (1. Nomura Asset Management Co., Ltd., 2. Hokkaido University, 3. UZABASE, Inc., 4. Osaka Metropolitan University)

Keywords:Skill Matrix, ESG, Financial Text Mining, Multi Label Classification, Natural Language Processing

We analyze the relationship between the disclosure of director skill matrices, corporate financial characteristics, and ESG scores in Japanese companies. Skill matrices have gained attention as a tool to enhance board functions. However, their creation is left to individual companies, leading to issues such as a lack of objectivity and standardization. Furthermore, we find that little empirical research has been conducted on the information value disclosed through these matrices.
In this study, we classified director skill matrices by extracting skills from publicly available nomination statements for directors. We created a dataset, defined the skills, and formulated the classification as a multi-label problem. To address label imbalance, we developed a learning framework and trained a model to classify the skill matrices.
We then analyze the relationship between these estimated skill matrices and corporate financial performance and ESG scores using data from TOPIX100 companies.
Our analysis shows that directors with financial, business management, and international experience are positively associated with ROE and governance scores.