1. Stock price crash data
1. Data source: third party
2. Time span: 2000-2020
3. Regional scope: A-share listed companies
4. Indicator description:
Refer to the latest literature to calculate the relevant measurement indicators for measuring stock price crashes
The specific indicators are as follows:
| NCSKEW |
Negative Degree of Skewness Coefficient of Corporate Stock Return |
| SIGMA |
The standard deviation of the weekly return of company i in year t |
| RET |
The average weekly rate of return of stock i in year t |
| DUVOL |
The degree of left skewness of the skewness coefficient of the company's stock return rate |
| CRASH |
The frequency measure coefficient of corporate stock crashes |
Part of the data is as follows:
Calculated references:
Si Dengkui, Li Xiaolin, Zhao Zhongkuang. Shadow banking of non-financial companies and stock price crash risk [J]. China Industrial Economy, 2021.
Lu Guihua, Pan Liuyun. Does Executive Academic Experience Affect Stock Price Crash Risk? [J]. Management Review, 2021.
Yi Zhihong, Wang Hao, Chen Qinyuan. Corporate External Guarantees and Stock Price Crash Risk——Based on Empirical Evidence of A-Share Listed Companies[J]. Accounting Research, 2021.
Peng Yuchao, Ni Xiaoran, Shen Ji. Enterprises' "Leaving Reality to Virtuality" and Financial Market Stability*——Based on the Perspective of Stock Price Crash Risk[J].Economic Research.2018(10):50-66.
2. Stock price synchronization data
1. Data source: same calculation references
2. Time span: 2000-2019
3. Regional scope: same as calculation references
4. Indicator description:
Calculated references:
"News Media Reports and Capital Market Pricing Efficiency——Analysis Based on the Synchronization of Stock Prices"
Part of the data is as follows:
Related research:
[1] Deng Yingxiang, Zhu Guilong. Research on China's industry-university-research cooperation based on patent data [J]. Science of Science and Management of Science and Technology, 2009, 30(012):16-19.
[2] Zhuang Tao, Wu Hong. Research on the triple helix measurement of government, industry, university and research based on patent data——Also discussing the role of government in industry-university-research cooperation [J]. Management World, 2013(08):175-176.
[3] Wang Banban, Qi Shaozhou. The effect of market-based and command-based policy tools on energy-saving and emission-reduction technology innovation——Based on the empirical evidence of China's industrial patent data [J]. China Industrial Economics, 2016(6):91-108.
[4] Huang Lucheng, Gao Shan, Wu Feifei, et al. Analysis of Global High-Speed Railway Technology Competition Situation Based on Patent Data [J]. Journal of Intelligence, 2014(12):41-47.