1. Impact of Environmental Policies on the Stock Prices of Energy Companies
Xiaoli Zhao
Ye Fan
Xueying Yu
Ming Fang
Oct
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

2. Outline Introduction Hypothesis Data Methodology Empirical results
Conclusions
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

3. Introduction
In traditional corporate management, less attention has been paid to the environmental performance of firms because of profit maximization.
However, stricter environmental policies and a growing concern from consumers have triggered a change from a simple corporate management to an environmentally conscious one.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

4. Environmental Policies the Stock Prices of Energy Companies
Introduction
Affect?
Environmental Policies
the Stock Prices of Energy Companies
Not Affect?
The reaction of Capital Market and Energy Companies
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

5. Hypothesis
Generally，environmental control policies have negative impact on stock price of listed energy companies in the short term. Because, when dealing with environmental policies, companies have to pay extra money on them, and the corporate earnings are reduced (Yanying Chen, 2009). Samiah (2013) has the idea that environmental control policies will increase company cost.
Hypothesis 1: Environmental control policies have negative impact on stock price of listed energy companies in the short term.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

6. Hypothesis
In China, traditional energy industries are mainly thermal power generation, coal and oil & gas, they also play important parts in listed energy companies. Coal companies can adjust the selling price according to the market. However, companies of thermal power generation and oil & gas are limited.
Hypothesis 2: In the short term, environmental policies has a greater negative impact on the stock prices of the electric industry and the oil industry rather than the coal industry.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

7. Hypothesis
Plumlee (2009) found companies of a high level of disclosure were welcomed by the capital market, because their expected cash flows would increase. Wang and Choi (2010),Yang Tang(2013) hold the same idea.
Hypothesis 3: In the short term , the negative impact of environmental policies is relatively low for the listed energy companies who possess a higher level of disclosure.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

8. Hypothesis
China has vast country lands, population density and unbalanced economic development. So the sense of protecting environment is different in the same way. The level of industrialization of listed energy companies in eastern and central regions of China is higher than the western companies, they need more money to deal with the new coming policies which will surely influence their economic development.
Hypothesis 4: Environmental control policies have greater negative effect on Listed Energy Companies in eastern and central regions of China than the western ones.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

9. Hypothesis
Cowen (1987) point out energy companies of large scale will be paid more attention by the public, so they deal with environmental issues actively. However, companies of small size are limited by financial strength, political and social pressure, their ability to deal with environmental issues is always challenged.
Hypothesis 5: The negative impact of environmental regulation policies on the listed energy companies of large scale is smaller than the companies of small size.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

10. Hypothesis
Our classification of environmental policies are Market stimulation, Administrative control, Legislative control and Disclosure. Administrative control, Legislative control are more powerful than Market stimulation and Disclosure (John J.Binder, 1985).
Hypothesis 6: The negative impact of environmental regulation policies of Market Stimulation and Disclosure is less than the policies of Administrative control and Legislative control.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

11. Data Environmental control policies over the years No. Enacted Time
Enacting Department
Environmental Policy
Policy Type 1
Jun
National Development and Reform Commission
Long-term development plan for renewable energy
Market stimulation 2
Nov
State council
Energy conservation and emissions reduction statistical monitoring and assessment of implementation plan and method
Administrative control 3
Jan
State Environmental Protection Department, NDRC
Pollution prevention of acid rain and carbon dioxide in the eleventh five-year plan 4
Aug
National People's Congress
People's Republic of China Circular Economy Promotion Law
Legislative control 5
Aug
Resolution on tackling climate change 6
Jan
Ministry of Environmental Protection
Publish environmental administrative penalties 7
May
Notification of "Further intensify efforts to ensure the realization of the emission reduction targets in the eleventh five-year plan" 8
Aug
Publish Main pollutants emission in China, 2011
Disclosure 9
Feb
Clean Production Promotion Law of People's Republic of China 10
Sep
Air Pollution Control Action Plan
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

12. Statistics of listed energy companies
表3-1 能源上市公司市值统计
Data
Statistics of listed energy companies
Classification
Number
The proportion of top 20 percent market capitalization companies
The average market capitalization($ hundred million)
Listed Energy Companies 52
89.10%
473.5
Industry
Electricity, heat production 33
38%
82.4
Coal Mining 15
71%
292.5
Oil, gas exploration 4
4378.6
The level of disclosure
High 19
90%
114.6
Low
61%
87.2
Area
East 30
764.2
Middle
48%
88.7
West 7
33%
52.2
Market capitalization ($ hundred million)
V<50 22
29.70%
26.4
50≤V<100 11
25.60%
69.6
V≤100
85.20%
1225.1
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

13. Data No. Code 27 600188 Yanzhou Coal Mining Company Limited 28 600348
000027
Shenzhen Energy Group Co., Ltd. 2
000037
Shenzhen Nanshan Power Co.,Ltd. 3
000531
Guangzhou Hengyun Enterprises Holding Ltd. 4
000539
Guangdong Electric Power Development Co.,Ltd. 5
000543
Anhui Wenergy Company,Limited 6
000552
Gansu Jingyuan Coal Industry and Electricity Power Co.,Ltd 7
000600
Jointo Energy Investment Co.,Ltd. Hebei 8
000601
Guangdong Shaoneng Group Co.,Ltd. 9
000690
Guangdong Baolihua New Energy Stock Co.,Ltd. 10
000692
Shenyang Huitian Thermal Power Co.,Ltd. 11
000695
Tianjin Binhai Energy & Development Co.,Ltd 12
000780
Inner Mongolia Pingzhuang Energy Resources Co.,Ltd. 13
000937
Jizhong Energy Resources Co., Ltd. 14
000966
Guodian Changyuan Electric Power Co.,Ltd. 15
000983
Shanxi Xishan Coal and Electricity Power Co.,Ltd 16
000993
Fujian Mindong Electric Power Limited Company 17
001896
Henan Yuneng Holdings Co.,Ltd. 18
002039
Guizhou Qianyuan Power Co.,Ltd. 19
600011
Huaneng Power International,Inc. 20
600021
Shanghai Electric Power Company Limited 21
600027
Huadian Power International Corporation Limited 22
600028
China Petroleum & Chemical Corporation 23
600098
Guangzhou Development Group Incorporated 24
600101
Sichuan Mingxing Electric Power Co.,Ltd. 25
600123
Shanxi Lanhua Sci-Tech Venture Co.,Ltd 26
600167
Luenmei Holding Co.,Ltd
No.
Code 27
600188
Yanzhou Coal Mining Company Limited 28
600348
Yang Quan Coal Industry （Group） Co., Ltd. 29
600395
Guizhou Panjiang Refined Coal Co.,Ltd. 30
600396
Shenyang Jinshan Energy Co.,Ltd. 31
600397
Anyuan Coal Industry Group Co., Ltd. 32
600508
Shanghai Datun Energy Resources Co.,Ltd. 33
600509
Xinjiang Tianfu Thermoelectric Co.,Ltd. 34
600578
Beijing Jingneng Thermal Power Co.,Ltd. 35
600583
Offshore Oil Engineering Co.,Ltd. 36
600719
Dalian Thermal Power Co.,Ltd. 37
600726
Huadian Energy Company Limited 38
600744
Datang Huayin Electric Power Co.,Ltd 39
600758
Liaoning Hongyang Energy Yesource Invest Co.,Ltd 40
600780
Top Energy Company Ltd.Shanxi 41
600795
GD Power Development Co.,Ltd. 42
600863
Inner Mongolia Mengdian Huaneng Thermal Power Corporatio 43
600864
Harbin Hatou Investment Co.,Ltd. 44
600971
Anhui Hengyuan Coal Industry and Electricity Power Co.,Ltd 45
600982
Ningbo Thermal Power Co., Ltd. 46
601001
Datong Coal Industry Co.,Ltd. 47
601088
China Shenhua Energy Company Limited 48
601666
Pingdingshan Tianan Coal. Mining Co.,Ltd. 49
601699
Shanxi Lu'An Environmental Energy Development Co.,Ltd. 50
601808
China Oilfield Services Limited 51
601857
Petrochina Company Limited 52
601991
Datang International Power Generation Co.,Ltd.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

14. Methodology
Event study is a research methodology designed to measure the impact of an event of interest on stock returns. Dolley (1933), Fama (1969), Mackinlay (1997) explains this method in detail. To date using a market model, several researchers have performed event studies on the day of the announcement pertaining to environmental information — Arora (2001), Gupta and Goldar (2005), and Klassen and McLaughlin (1996).
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

15. Methodology Event study contains six steps.
1. Define event and time window
2. Identify research samples
To examine the effect on share prices, daily data was obtained from CCER for each of the 10 events of the 52 companies.
Estimation
period
Event
day
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

16. Methodology
3. Determine share returns model. From the raw daily data, share returns were calculated according to the formula:
where is the share return for firm i on day t and
is the share price for day t.
(1)
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

17. Methodology 4. Estimate the abnormal returns.
The expected returns is obtained from the market model and EGARCH model (Nelson,1991). The abnormal returns ARi,t is obtained from the formula 4.
(2)
(3)
(4)
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

18. Methodology
stands for the cumulative abnormal returns from t1 to t is abnormal returns.
Also take weight of companies into consideration.
(5)
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

19. Methodology
5. Inspection of significant abnormal returns. To test the null hypothesis that the event does not affect the returns, we use the following J-statistics: 6. Conclusion.
(6)
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

20. CAR(cumulative abnormal returns ) of all the listed energy companies
Empirical results
CAR(cumulative abnormal returns ) of all the listed energy companies
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

21. Empirical results
Significance Testing – Summary
classification
J-5
J-15
J-30
Industry
Electricity, heat production
***
***
***
Coal Mining
***
***
***
Oil, gas exploration *
-1.81*
The level of disclosure
High
***
***
***
Low
***
***
***
Area
East
*** ** **
Middle
***
***
***
West
***
***
***
Market capitalization ($ hundred million)
V<50
***
***
***
50≤V<100
***
***
***
V≤100
***
*** **
J-5，J-15，J-30 stand for the J number in significance test in date [-5，5]，[-15，15]，[-30，30]
* = significant at the 1% level.
** = significant at the 5% level.
*** = significant at the 10% level.
CAR(cumulative abnormal returns ) of all the listed energy companies
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

22. Summary figure of CAR in different industries
Empirical results
Summary figure of CAR in different industries
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

23. Summary figure of CAR in different level of disclouse
Empirical results
Summary figure of CAR in different level of disclouse
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

24. Summary figure of CAR in different area
Empirical results
Summary figure of CAR in different area
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

25. Summary figure of CAR in different size of companies
Empirical results
Summary figure of CAR in different size of companies
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

26. Empirical results
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

27. Empirical results Significance Testing - Market Incentive
classification
J-5
J-15
J-30
Industry
Electricity, heat production
Coal Mining
Oil, gas exploration
The level of disclosure
High
Low
Area
East
Middle
West
Market capitalization ($ hundred million)
V<50
50≤V<100
V≤100
J-5，J-15，J-30 stand for the J number in significance test in date [-5，5]，[-15，15]，[-30，30]
* = significant at the 1% level.
** = significant at the 5% level.
*** = significant at the 10% level.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

28. Empirical results Significance Testing - Legislative Control
classification
J-5
J-15
J-30
Industry
Electricity, heat production *
Coal Mining *
Oil, gas exploration
The level of disclosure
High
*** **
***
Low *
Area
East
Middle *
West
Market capitalization ($ hundred million)
V<50
***
*** **
50≤V<100
V≤100 *
J-5，J-15，J-30 stand for the J number in significance test in date [-5，5]，[-15，15]，[-30，30]
* = significant at the 1% level.
** = significant at the 5% level.
*** = significant at the 10% level.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

29. Empirical results Significance Testing - Administrative Control
classification
J-5
J-15
J-30
Industry
Electricity, heat production
***
***
***
Coal Mining
***
***
***
Oil, gas exploration
The level of disclosure
High
***
***
***
Low
***
***
***
Area
East ** ** **
Middle
***
***
***
West **
***
***
Market capitalization ($ hundred million)
V<50
***
***
***
50≤V<100
***
***
***
V≤100 ** ** **
J-5，J-15，J-30 stand for the J number in significance test in date [-5，5]，[-15，15]，[-30，30]
* = significant at the 1% level.
** = significant at the 5% level.
*** = significant at the 10% level.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

30. Empirical results Significance Testing - Disclosure classification J-5
Industry
Electricity, heat production *
***
Coal Mining **
***
Oil, gas exploration
The level of disclosure
High
***
***
Low **
***
Area
East *
Middle **
***
West **
Market capitalization ($ hundred million)
V<50
***
***
50≤V<100
V≤100 * **
J-5，J-15，J-30 stand for the J number in significance test in date [-5，5]，[-15，15]，[-30，30]
* = significant at the 1% level.
** = significant at the 5% level.
*** = significant at the 10% level.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

31. Empirical results 0.172228 -0.07932 -0.21596 -4.96891*** -4.72453***
不同类型政策影响的显著性检验
Empirical results
Significance Testing
Environmental Policies
J-5
J-15
J-30
Market Incentive
Administrative Control
***
***
***
Legislative Control ** *
Disclosure **
***
J-5，J-15，J-30 stand for the J number in significance test in date [-5，5]，[-15，15]，[-30，30]
* = significant at the 1% level.
** = significant at the 5% level.
*** = significant at the 10% level.
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing

32. End Thank you all
Developed by: © Ye Fan, School of Business Administration, China University of Petroleum, Beijing