USING PATENT DRAWINGS OF INVENTION PUBLICATIONS TO DIFFERENTIATE STOCK RETURN RATE - AN EMPIRICAL STUDY ON CHINA STOCK MARKET

Hong-Wen Tsai (), Hui-Chung Che () and Bo Bai ()
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Hong-Wen Tsai: National Taiwan University of Science and Technology, Taiwan
Hui-Chung Che: Shenzhen TekGlory Intellectual Property Data Technologies, Ltd., China
Bo Bai: Shenzhen TekGlory Intellectual Property Data Technologies, Ltd., China

Eurasian Journal of Business and Management, 2022, vol. 10, issue 1, 37-61

Abstract: Patent is an important outcome of technological innovation, which drove economic growth. Though patent claim always caught attention when considering patentability, it had to be supported by the drawings according to the patent examination criteria. However, patent drawing was seldom discussed. More than 60% of China listed companies of RMB common stocks (A-shares) from 2017Q1 to 2021Q4 were selected as effective samples based on the company integrated database. The effect of China invention publication’s patent drawing count for differentiating China A-share’s stock return rate was thoroughly discussed via analysis of variation (ANOVA). The average drawing count and the total drawing count of invention publications significantly increased over previous five years even under the impact of COVID-19 pandemic. Moreover, the total drawing count was found to be an appropriate patent indicator for differentiating A-share’s stock return rate whereas the average drawing count was not because the average drawing count did not show significant connection with the stock return rate. The Ashares in the group of the highest total drawing counts showed significantly higher stock return rate mean while the A-shares in the groups of lower total drawing counts showed significantly lower stock return rate means in most quarters from 2017Q1 to 2021Q4. The finding also proved that the patent quantity still mattered in China stock market.

Keywords: Patent; Analysis of Variation; ANOVA; Stock Return Rate; Drawing Count; Invention Publication (search for similar items in EconPapers)
Date: 2022
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