| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T268 |
0-2 |
Sentence |
denotes |
6. |
| T269 |
3-45 |
Sentence |
denotes |
Additional Analyses and Sensitivity Checks |
| T270 |
47-51 |
Sentence |
denotes |
6.1. |
| T271 |
52-95 |
Sentence |
denotes |
Continued Decrease of Public Health Threats |
| T272 |
96-253 |
Sentence |
denotes |
To triangulate our results, we identify a situation that the firms located in a province where the threats of local public health are continually decreasing. |
| T273 |
254-436 |
Sentence |
denotes |
The argument underlying H1 is that provincial increasing public health threats will lead to a lower level of accumulative abnormal return by enhancing the investors’ risk assessment. |
| T274 |
437-718 |
Sentence |
denotes |
Presumably, if the province faces continued zero cases for newly confirmed COVID-19 that it does not have an adverse impact on decreasing public health threats to the local firms, investors will restrain the extent of risk assessment and investor trust can be expected to increase. |
| T275 |
719-857 |
Sentence |
denotes |
Thus, we conjecture that continued decrease of provincial public health threats is positively related to the accumulative abnormal return. |
| T276 |
858-958 |
Sentence |
denotes |
For testing this assumption, we substitute the CIPHT with CDPHT in Model (2) and run the regression. |
| T277 |
959-1211 |
Sentence |
denotes |
Here, CDPHT represents continued decreasing public health threats measured as an indicator variable that equals one if there have not been any provincial new COVID-19 cases for at least six consecutive days including the current day and zero otherwise. |
| T278 |
1212-1261 |
Sentence |
denotes |
Table 10 presents the results of this assumption. |
| T279 |
1262-1394 |
Sentence |
denotes |
We find a positive and significant coefficient on CDPHT in Columns (A) and (B), respectively, which consistent with our predictions. |
| T280 |
1396-1400 |
Sentence |
denotes |
6.2. |
| T281 |
1401-1441 |
Sentence |
denotes |
The Effectiveness of Community Lockdown: |
| T282 |
1442-1474 |
Sentence |
denotes |
Pre- versus Post-Lockdown Period |
| T283 |
1475-1646 |
Sentence |
denotes |
Considering the speedily spreading of COVID-19 in Wuhan province, Chinese governance decided to restrict human mobility by ordering a Wuhan lockdown since 23 January 2020. |
| T284 |
1647-1753 |
Sentence |
denotes |
Moreover, China extends lockdown to more areas by implementing the “closed community management” measures. |
| T285 |
1754-1828 |
Sentence |
denotes |
In February 2020, many provinces had selected the community lockdown mode. |
| T286 |
1829-1913 |
Sentence |
denotes |
Prior research [9] finds that lockdown effectively mitigated the spread of COVID-19. |
| T287 |
1914-2048 |
Sentence |
denotes |
We expect investors may notice the positive effects of lockdown and will restrain the risk assessment during the post-lockdown period. |
| T288 |
2049-2141 |
Sentence |
denotes |
For testing this assumption, we substitute Conditioning_VAR in Model (3) with POST_Lockdown. |
| T289 |
2142-2309 |
Sentence |
denotes |
Here POST_Lockdown is an indicator variable that equals one if the firm is in periods after implementing the "closed community management" measures and zero otherwise. |
| T290 |
2310-2389 |
Sentence |
denotes |
The information of the lockdown periods by province is shown in the Appendix B. |
| T291 |
2390-2699 |
Sentence |
denotes |
Table 11 shows the regression results, and we find that the coefficients of CIPHT × POST_Lockdown in both columns are positive and significant, which consistent with our assumption that community lockdown could mitigate the effect of continued increasing public health threats on accumulative abnormal return. |
| T292 |
2701-2705 |
Sentence |
denotes |
6.3. |
| T293 |
2706-2746 |
Sentence |
denotes |
The Impact of Firm-Level Characteristics |
| T294 |
2747-2876 |
Sentence |
denotes |
In an additional sensitivity test, we examine the firm heterogeneity in the effect of continued increasing public health threats. |
| T295 |
2877-3100 |
Sentence |
denotes |
The first assumption is that firms with a lower level of local consumer demand or geographical concentration of local businesses are more likely to mitigate the business risk raised by local public health threat [60,61,62]. |
| T296 |
3101-3338 |
Sentence |
denotes |
The second assumption is that firms with higher levels of operating cash flow are more likely to overcome the difficulty during the COVID-19 outbreak by improving the supply chain risk management [63] and investment diversification [64]. |
| T297 |
3339-3574 |
Sentence |
denotes |
The third assumption is that compare to the firms with non-clean audit opinions, the firms with clean auditor opinions on their financial reports are more likely to gain investor trust by showing reliable financial information [65,66]. |
| T298 |
3575-3787 |
Sentence |
denotes |
Following these arguments, we predict that firms with a higher level of foreign sales, operating cash flow, and with clean audit opinions are more likely to receive investor trust and have lower management risks. |
| T299 |
3788-3902 |
Sentence |
denotes |
As such, continued increasing provincial public health threats is less useful for firms with such characteristics. |
| T300 |
3903-4028 |
Sentence |
denotes |
To test our assumption, we substitute Conditioning_VAR in Model (3) with High_FSALE, High_OPCF, and Clean_OPIN, respectively. |
| T301 |
4029-4517 |
Sentence |
denotes |
Here, High_FSALE is an indicator variable that equals one if the firm’s foreign sales are higher than or equal to the upper quartile value and zero otherwise; High_OPCF is an indicator variable that equals one if the ratio of the firm’s operating cash flow to total assets is higher than or equal to the upper quartile value and zero otherwise; and Clean_OPIN is an indicator variable that equals one if the firm received a clean audit opinion for its financial report and zero otherwise. |
| T302 |
4518-4610 |
Sentence |
denotes |
Table 12 shows the regression results of the moderate effects of firm-level characteristics. |
| T303 |
4611-4970 |
Sentence |
denotes |
We find that the coefficients of CIPHT × High_FSALE, CIPHT × High_OPCF, and CIPHT × Clean_OPIN are all positive and significant, which is consistent with our prediction that geographical diversification, cash flow efficiency, and reporting quality could mitigate the negative effect of continued increasing provincial public health threats on market reaction. |
| T304 |
4972-4976 |
Sentence |
denotes |
6.4. |
| T305 |
4977-5046 |
Sentence |
denotes |
The Impact of Volatility of Provincial Increase in New COVID-19 Cases |
| T306 |
5047-5321 |
Sentence |
denotes |
As a final robustness check, we examine whether our results are influenced by the volatility of provincial increase in new confirmed COVID-19 cases because high volatility of the number changing in the new confirmed cases may affect the extent of investors’ risk assessment. |
| T307 |
5322-5411 |
Sentence |
denotes |
To tackle this concern, we substitute Conditioning_VAR in Model (3) with High_Volatility. |
| T308 |
5412-5615 |
Sentence |
denotes |
Here High_Volatility is an indicator variable that equals one if the six-day standard deviation of the new confirmed COVID-19 cases is higher than or equal to the upper quartile value and zero otherwise. |
| T309 |
5616-5727 |
Sentence |
denotes |
Table 13 shows the regression results on the moderate effect of volatility of the new confirmed COVID-19 cases. |
| T310 |
5728-5932 |
Sentence |
denotes |
We find that the coefficients of CIPHT × High_Volatility are all statistically insignificant, suggesting that our results are not driven by the fluctuations in the number of new confirmed cases over time. |
