Discuss Corporate Governance Performance with Tobin’s Q and ROI: Using Data Mining Technology

• Main Authors: Wang, Hui-Xian, 王慧仙
• Other Authors: 黃木榮
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/zkqz4j

碩士 === 國立彰化師範大學 === 會計學系 === 106 === In recent years, "Artificial Intelligence" has become a popular topic, and big data and data mining are the embodiment of Artificial Intelligence. From technology development, medical treatment, crime crackdown to the discovery of business trends, it is impossible to ignore their importance. Equity investment is one of the ways that many people do wealth management, but most investors only use the technical side to make investment judgments. This has led to many corruption cases that have caused investors to lose money. From the perspective of corporate governance, we can see the company's physique, which is also an important reference index for investors. However, there are many kinds of corporate governance factors, and it is impossible for every one of them to be comprehensive. Therefore, this study uses data mining technology to explore for Tobin's Q and ROI, which are important corporate governance judgment factors. Providing the most important indicators for decision makers. The research object of this study is the electronics industry in the first to third corporate governance evaluations published by the Securities & Futures Institute. The study found that: (1) Tobin’s Q’s performance is poor for companies with debt ratios of 50% or more and low research development.(2)ROA is better that companies with higher Primary Professional Investment; if Primary Professional Investment is low but Debt ratio is less than 50% and Pledge Ratio of Directors and Supervisors is less than 33%, ROA performance is also good. (3) ROE is better when Primary Professional Investment and Management Ownership are both high. (4) Corporate governance evaluation is a non-important decision factor for Tobin’s Q and ROE.