模糊邏輯在公司治理評等系統之應用

Abstract

本研究之目的在解決國內外公司治理評等系統中主觀上決定權重的問題，並以里昂證券的公司治理評等系統與葉銀華等學者的台灣公司治理評等系統為改良之系統，將模糊邏輯技術整合在各別評等系統中，解決公司治理程度多好才是「好」或多差才是「差」的不確定及模糊不清的問題。 本研究首先建立這兩公司治理評等系統各別的模糊邏輯模型，將其各別公司樣本的考量指標分數當作輸入值以得各別公司樣本之模糊邏輯公司治理指標。研究進一步驗證其與公司經營績效之關聯性。研究結果發現，這兩公司治理評等系統的模糊邏輯之公司治理指標皆與各別原來的加權平均之公司治理指標有明顯的不同。在檢測其與公司經營績效的關聯性時，顯示模糊邏輯應用在公司治理評等系統中，確實可以消除主觀上決定權數的問題，模糊邏輯之公司治理指標能較原本的加權平均之公司治理指標客觀，更能解釋當年與未來的公司績效。

The purpose of this thesis is to solve the problem of subjective weighting judgments in the domestic and overseas corporate governance rating systems and to take CLSA corporate governance rating system and Yeh et al.’s Taiwan corporate governance rating system as the improvable systems. Integrating the fuzzy logic technique into each corporate governance rating system is to solve the uncertainty and the vagueness of how good or how bad in the degree of corporate governance. At first, this thesis builds each fuzzy logic model for these two corporate governance rating systems. The fuzzy logic models treat the scores of indicators in these two corporate governance rating systems as the input values in order to output the fuzzy logic corporate governance indicators. Furthermore, the thesis verifies the correlation between fuzzy logic corporate governance indicators and the performance of firms. The empirical results show that each fuzzy logic corporate governance indicator is quite different from each original weighted corporate governance indicator in these two corporate governance rating systems. The empirical results of multiple regressions imply that the fuzzy logic tool can solve the problem of subjectively weighting judgments when it applies in the corporate governance rating system. The fuzzy logic corporate governance indicator is more objective than the weighted corporate governance indicator. Its explanation ability in the performance of firms is stronger than the weighted corporate governance indicator’s.