The Impact of Patents on Performance of Seasoned Equity Offerings

碩士 === 國立成功大學 === 國際企業研究所 === 103 === It is an effective way for firms to raise external capital through seasoned equity offerings (SEO). However, SEO usually regards as a negative signal to outside investors and then causes SEO announcement effect and long-run underperformance. The main reason is b...

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Bibliographic Details
Main Authors: Yi-NingChen, 陳怡寧
Other Authors: Shao-Chi Chang
Format: Others
Language:en_US
Published: 2015
Online Access:http://ndltd.ncl.edu.tw/handle/30369238225437945952
Description
Summary:碩士 === 國立成功大學 === 國際企業研究所 === 103 === It is an effective way for firms to raise external capital through seasoned equity offerings (SEO). However, SEO usually regards as a negative signal to outside investors and then causes SEO announcement effect and long-run underperformance. The main reason is because of information asymmetry. Therefore, managers are willing to provide some evidence or signals to outside investors to reduce information asymmetry. On the one hand, previous studies suggest that patents exist certificate effect, contain sustained competitive advantage and could convey credible information to investors. As a result, patents could reduce information asymmetry between firms and outside investors. On the other hand, some researchers mentioned that patents do not convey any information about the business aspect and the patent quality is another factor to influence firms’ performance. Therefore, they find unclear relationship between patents and information asymmetry. This study aims to investigate whether patents could reduce information asymmetry and then how to affect SEO announcement return and long-run performance. 1,368 firms that issued SEO during 2000 to 2013 in the U.S. are selected into analysis in this study. The regressions result shows that patent counts is negative and significantly associated with SEO announcement return. The result is also the same in the long-term. Therefore, it is suggested that patents could not reduce information asymmetry. Furthermore, I classified the sample into high-patent group and low-patent group and then find that low-patent group performed well than high-patent group.