以基本面分析強化社會責任投資績效

Abstract

本研究以台灣環境、社會與公司治理（Environmental, Social, and Corporate Governance，簡稱ESG）上市公司為研究對象，探討財報基本面資訊與橫斷面股票報酬之間的關係。本研究參考Yan and Zheng（2017）之分析架構，建立7,180個財務信號（fundamental signals），並利用這些基本面分析信號構成ESG股票的投資組合，測試這些投資組合在風險調整後是否能產生顯著超額報酬。本研究結果發現，在等權重投資組合的方式下，採用法定盈餘公積的會計信號，經風險調整後的ESG投資組合月報酬可達1.27%。若改用市值加權方式，搭配稅後淨利的會計信號，經風險調整後的ESG投資組合月報酬更可提升至1.70%。本研究結果顯示針對財報基本面資訊進行資料探勘（data-mining），有助於提升ESG投資組合績效。

This study examines the relationship between financial reporting fundamental information and cross-sectional stock returns of Environmental, Social, and Governance (ESG) firms listed in Taiwan Stock Exchange (TWSE). Based on the analytical framework of Yan and Zheng (2017), 7,180 fundamental signals were established, and these fundamental signals were used to construct a portfolio considering ESG stocks to test whether these portfolios could generate significant excess returns after risk adjustment. The empirical results show that the risk-adjusted monthly return of the ESG portfolio can reach 1.27% using the accounting signal of legal reserve under the equal -weighted portfolio approach. The risk-adjusted monthly return of the ESG portfolio could rise to 1.70% if the market value weighted method is used when combined with the accounting signal of after-tax net income. This study also shows that data mining based on the fundamental information of financial statements is helpful to improve the performance of ESG portfolio.