2021年拉闸限电事件对中国股市的冲击

doi:10.16381/j.cnki.issn1003-207x.2022.1731

Abstract

Abstract:

The power-rationing event implemented in many provinces in 2021 is the first major event that China has encountered in the process of economic green and low-carbon transformation under the background of the “double carbon” strategy， and it can be regarded as a natural experiment to affect the Chinese stock market. Will the power-rationing event have a significant impact on the Chinese stock market？ How will investors choose under the impact of power-rationing？ Will this event change people’s confidence in China’s economic development？ To answer these important questions， 48 trading days from August 1st， 2021， to October 15th， 2021， are selected as the sample period to study the impact of the power-rationing event on China's A-share market by using the event analysis model， time-varying Difference-in-Differences and Difference-in-Differences-in-Differences models. The specific empirical work and the empirical conclusions in this article are as follows： First， based on the event analysis model， the impact of power rationing on different industries in power-restricted provinces is examined. The results show that after companies in high-energy-intensity industries are affected by power rationing， the stock price change， abnormal return， and institutional net buying orders decrease significantly； at the same time， companies in non-high-energy-intensity industries in power-restricted provinces are obviously affected by power rationing. However， the specific direction of the impact is not consistent with companies in high-energy-intensity industries. Second， based on the time-varying Difference-in-Differences model， whether the power rationing event had a significant impact on the A-share listed companies in power-restricted provinces is tested. The results show that the power-rationing event has a significant positive impact on the stock price change and abnormal return of companies located in power-restricted provinces， which are mainly driven by the rise in stock in non-high-energy-intensity industries in power-restricted provinces. Further testing of the dynamic effects shows that the power rationing had no long-term impact on the stock market. Third， based on the Difference-in-Differences-in-Differences model， the heterogeneity of investor buying behavior in the impact of power rationing on the stock market is examined. The results show that the positive short-term impact of the events on the stock price change and abnormal return in the restricted provinces is mainly driven by the investment behaviors of institutional investors and big investors. The events make these investors increase the net buy of stocks in non-high-energy-intensity industries in the power-restricted provinces. To sum up， the research in this article shows that the securities market has maintained smooth operation under the impact of power rationing， reflecting investors' stable expectations for the long-term optimism of the securities market and their firm confidence in the green transformation under the dual-carbon strategy. To a certain extent， it proves that China's economy has strong resilience in dealing with shocks.

Key words: power-rationing events, price change, abnormal return, time-varying difference-in-differences model, difference-in-differences-in-differences model

CLC Number:

F832.51

Jixian Meng,Jijia Kang,Qianqian Yang, et al. The Impact of Power Rationing Events in 2021 on China's Stock Market[J]. Chinese Journal of Management Science, 2026, 34(3): 25-38.

Figures/Tables 13

符号	变量名称	均值	标准差	最小值	最大值
PC	涨跌幅	0.101	3.332	-20.006	20.118
AR	超额收益率	0	0.030	-0.205	0.236
NBII	机构投资者净买单量	-18.268	258.312	-8929	7028
NBBI	大户投资者净买单量	-50.895	339.935	-8583	6109
NBMI	中户投资者净买单量	-30.206	573.822	-32616	25947
NBPI	散户投资者净买单量	1126.163	7044.264	-156221	340310
$D$	事件冲击的交互项	0.241	0.428	0	1
EII	是否高耗能行业	0.204	0.403	0	1
SOE	是否国有企业	0.293	0.455	0	1
HS300	是否沪深300成分股	0.075	0.264	0	1
TR	换手率	2.724	3.182	0.023	47.438
MV	市值	22.885	1.136	20.769	28.517
R&D	研发投入	4.702	5.481	0	80.834
ROE	净资产收益率	5.796	10.016	-26.894	398.312
TA	有形资产	46.205	21.791	-45.776	96.876
PB	市净率	4.208	8.043	-78.447	379.821
PE	市盈率	36.999	726.238	-57131.328	13989.527
Lev.	杠杆率	2.555	30.020	1.002	1527.941

变量	(3.1) PC	(3.2) PC	(3.3) AR	(3.4) AR
$D i, t$	0.063^* (1.81)	0.056^* (1.65)	0.075^** (2.22)	0.070^** (2.09)
SOE		0.131^*** (5.90)		0.071^*** (3.26)
HS300		-0.001 (-0.02)		0.161^*** (3.95)
TR		0.168^*** (59.08)		0.132^*** (47.39)
MV		0.074^*** (7.03)		0.026^** (2.50)
R&D		-0.007^*** (-3.54)		-0.002 (-1.13)
ROE		-0.001 (-1.47)		-0.001 (-1.58)
TA		0.000 (1.08)		0.001^*** (3.13)
PB		-0.001 (-0.85)		-0.005^*** (-3.14)
PE		0.000^** (2.32)		0.000 (0.90)
Lev.		-0.000 (-0.18)		0.000 (0.47)
常数项	2.340^*** (4.66)	-0.649 (-1.26)	-0.180 (-0.37)	-1.820^** (-3.37)
时间 FE	是	是	是	是
省份 FE	是	是	是	是
行业 FE	是	是	是	是
观测值	133152	133152	133152	133152
R²	0.130	0.153	0.004	0.020

变量	(4.1) PC	(4.2) PC	(4.3) AR	(4.4) AR
$D i, t$	0.047 (0.56)	0.029 (0.34)	0.062 (0.76)	0.049 (0.60)
控制变量	否	是	否	是
常数项	是	是	是	是
时间 FE	是	是	是	是
省份 FE	是	是	是	是
行业 FE	是	是	是	是
观测值	118637	118637	118615	118615
R²	0.072	0.075	0.002	0.004

变量	高耗能行业
变量	(5.1) PC	(5.2) AR
$D i, t × E I I i$	-0.719^*** (-15.00)	-0.521^*** (-11.06)
$D i, t$	0.218^*** (6.10)	0.187^*** (5.33)
控制变量	是	是
常数项	是	是
时间 FE	是	是
省份 FE	是	是
行业 FE	是	是
观测值	133152	133152
R²	0.154	0.021

变量	(6.1) NBII	(6.2) NBBI	(6.3) NBMI	(6.4) NBPI
$D i, t$	4.931^* (1.74)	6.328^* (1.72)	13.818^** (2.17)	-15.928 (-0.21)
控制变量	是	是	是	是
常数项	是	是	是	是
时间 FE	是	是	是	是
省份 FE	是	是	是	是
行业 FE	是	是	是	是
观测值	133152	133152	133152	133152
R²	0.031	0.060	0.011	0.064

变量	(7.1) NBII	(7.2) NBBI	(7.3) NBMI	(7.4) NBPI
$D i, t × E I I i$	-28.294^*** (-7.11)	-53.587^*** (-10.40)	12.525 (1.40)	769.082^*** (7.22)
$D i, t$	11.297^*** (3.81)	18.384^*** (4.78)	11.000^* (1.65)	-188.960^** (-2.37)
控制变量	是	是	是	是
常数项	是	是	是	是
时间 FE	是	是	是	是
省份 FE	是	是	是	是
行业 FE	是	是	是	是
观测值	133152	133152	133152	133152
R²	0.032	0.061	0.011	0.065

变量	PC				AR
变量	(8.1) day_3	(8.2) day_2	(8.3) day2	(8.4) day3	(8.5) day_3	(8.6) day_2	(8.7) day2	(8.8) day3
$D i, t$	0.012 (0.31)	0.011 (0.30)	-0.050 (-1.19)	-0.062 (-1.42)	0.037 (1.00)	0.040 (1.06)	-0.020 (-0.48)	-0.023 (-0.53)
控制变量	是	是	是	是	是	是	是	是
常数项	是	是	是	是	是	是	是	是
时间 FE	是	是	是	是	是	是	是	是
省份 FE	是	是	是	是	是	是	是	是
行业 FE	是	是	是	是	是	是	是	是
观测值	116508	116508	116508	116508	116508	116508	116508	116508
R²	0.159	0.159	0.159	0.159	0.024	0.024	0.024	0.024

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组别	限电省/市/自治区	事件发生时间
第一组	河南，重庆，宁夏，四川	8月16日
第二组	青海，广西，新疆，内蒙古	8月25日
第三组	吉林，黑龙江，贵州，云南，山东，陕西	9月13日
第四组	江苏，广东，浙江，安徽，湖南，辽宁	9月23日

The Impact of Power Rationing Events in 2021 on China's Stock Market

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