Transcript
Stock performance and air pollution Dingteng Huang
Shuang LiangNan Li
Puyi FangYiwei Yan
Rapid economic growth in Asia, especially in China.
• China is the world’s second largest economy• GDP growth rate is about 7% in 2014• ShangHai is the the economic center of China• ShangHai Stock Exchange
Terrible air quality in China: especially Beijing and ShangHai
• Most important air quality figures : PM 2.5• Other parameter : PM10, SO2, NO2 • Influence on people work and life
Background
Object and Hypothesis
Object
• Observe and analyze the relationship between stock returns of different industries and air quality in Shanghai
Hypothesis
• Air quality, especially PM 2.5 has a significant effect on various stock returns.
• The more terrible the air quality is, the higher the stock return in heavy industry.
Data Collection
Time period: 2012 -2015
Stock Price Data
• daily adjusted close prices of different industries• Discrete and log returns of stock price
Air Quality Data
• Daily Average PM 2.5• Daily Max PM 2.5• Daily Average PM 10• Daily Average SO2• Daily Average NO2
Key Variables
Dependent Variable
• SSEDiscete-n is the discrete stock return of SSE• SPCReturn is Shanghai petrochemical company stock return• SHEReturn is Shanghai Electric Power stock return• SPEReturn is Shanghai DaTun Energy Company stock return• SEEPostive-n is a binary variable that equal 1 if SSE stock return
is positive, otherwise is zero
Independent Variables
• PM25Average is the Average daily figure of PM 2.5• PM25Max is the highest PM 2.5 reached each day• PM10 is the Average daily figure of PM 10• SO2 is the Average daily figure of SO2• NO2 is the Average daily figure of NO2
Key Variables
Seasonality
• Q2, Q3, Q4• Represents the second quarter, third quarter, and fourth quarter of a year
respectively • Dummy Variable• Base group : Q1• The seasonality has a significant impact on both stock price and air quality
Unheal-25100
• Represents if PM 2.5 is greater than 100, it is unhealthy.• Dummy Variable
• No previous research on the same topic
• First, we need to run every possible regression from all the variables we have in order have a base data group.
• Co-linearity Problem
Research Logic
Co-linearity Problem
Mean VIF 3.64 Q4 1.72 0.582712 Q2 1.75 0.571621 NO2 1.97 0.507927 Q3 2.04 0.489939Unheal~25100 2.40 0.416220 SO2 2.75 0.364157 PM25Max 5.19 0.192731 PM10 5.27 0.189702 PM25Average 9.67 0.103366 Variable VIF 1/VIF
. vif
.
_cons 0.2511 -0.2400 -0.2425 -0.2330 -0.3877 -0.5042 -0.5346 -0.2332 0.1833 1.0000 Unheal~25100 -0.0824 -0.3457 0.0303 -0.1256 -0.0602 0.0029 0.0397 0.0154 1.0000 Q4 0.1745 0.0187 -0.2059 -0.1553 -0.0270 0.4551 0.4684 1.0000 Q3 0.1875 0.0274 -0.3183 0.1689 0.1806 0.5738 1.0000 Q2 0.0160 0.0878 -0.2192 0.2410 0.1319 1.0000 NO2 -0.1503 0.0700 -0.2196 0.0170 1.0000 SO2 -0.1726 0.0570 -0.2541 1.0000 PM10 -0.5582 0.0438 1.0000 PM25Max -0.5891 1.0000 PM25Average 1.0000 e(V) PM25Av~e PM25Max PM10 SO2 NO2 Q2 Q3 Q4 Un~25100 _cons
Correlation matrix of coefficients of regress model
• Solution to Co-linearity Problem (orthogonalize)
– Regression PM2.5 vs. PM2.5Max
– Regression PM2.5 vs. PM10
– Use residuals to replace PM2.5Max and PM10
• 3 Stage filtering
Research Logic
Base Regression
Group Advance Regression
Group
Log vs. SimpleLag 0 day vs.1day
vs. 3day
Final Regression
Group
Drop and modify Variables base on
F-test
• Same day simple return
• Shanghai Bao Steel Company daily return vs. PM2.5
• BaoReturn = 0.0002857 – 0.00153*PM2.5Averag + 0.001205*Q3 + 0.0037935*Q4 + μ
(0.0011943) (0.00153) (0.00137) (0.00133)
• F(3,721) = 2.91 significant at 95%• Steel industry has always been a large source of pollutions historically• that PM2.5 index has an negative impact on the return of Bao Steel• showing the market’s reaction towards air pollution in Shanghai
Regression Analysis & Finding
Tier 2
Lag one day OLS Regression
• SSEDiscreteReturn = -0.0016134 + 0.00507*S02 + 0. 00197*N02 (0.0011369) (0.00409) (0.00201) + + + 0.0013873*Q3 + 0.001821*Q4 – 0.0022324*UnhealthyPM2.5+ μ (0.001049) (0.001087) (0.0011373)
-• F-stat(5,710) of 2.10 which is significant at 94% confidence
level.
• A negative beta of unhealthy PM 2.5 index, that is significant at 95%, aligned with our hypothesis that, if the former day’s PM 2.5 index exceed the healthy level, for the following day the return of SSE Composite Index daily return will decrease by 0.22324%.
SSE Composite Index Return (000001.SS)
• SPCReturn = -0.0021294 + 0. 00975*PM10 + 0. 0083*NO2 –0.0045505*UnhealthyPM2.5+ μ (0.0022307) (0.00578) (0.00449) (0.0026083)
+ + -• F-stat(3,720) of 2.14 which is significant at 91% confidence level. • A positive beta of PM 10 index and daily average NO2, that is
significant at 90% confidence level. • For the following day, one mg/m3 increase in PM 10 will positively
impact the return of SPC daily return by 0. 975% One 1 mg/m3 increases in NO2 will positively impact the return of
SPC daily return by 0.83%.
• SPC is the largest petrochemical company located in Shanghai, which engaged in production of ethylene, fiber, resin and plastics. One of main pollution sources of NO2 and PM 10 in Shanghai are come from SPC.
Shanghai Petrochemical company
SHEReturn = -0.0045377 – 0.0037*PM2.5Max + 0.00299*PM10 + 0.01808*SO2 (0.0021054) (0.00447) (0.00433) (0.00709)
+0.00239*NO2 + 0.0034606*Q3 – 0.0060768*UnhealthyPM2.5 + μ (0.00363) (0.0018325) (0.002122)
• F-stat(6,717) of 2.31 which is significant at 95% confidence level.
•A positive beta of SO2, that is significant at 99% confidence level (T-stat = 2.55), also a negative beta of Unhealthy PM 2.5 index(T-stat = -2.88), that is significant at 99% confidence level.
• SHE is the largest electric power company located in Shanghai. The coal burning plants are the biggest pollution source of PM2.5 and NO2. The biggest coal consuming factory at Shanghai is the SHE company
Shanghai Electric Power Company
•PM2.5PortReturn = 0.0031607 + 0.00462*PM2.5 – 0.01482*SO2 – 0.0022936*Q2 + μ (0.0021151) (0.00395) (0.001094) (0.0024741)
+ + -
This is the only model that the coefficient of SO2 is negative and the intercept is a positive number.
PM2.5 Portfolio
Tier 3Binary Dependent Variable Probit Regression
Shanghai Electric Power (SHE)
Screening rules: |Z|>1.96, high confidence level
Daily average SO2, 96% confidence level
Increase the probability of positive return of SHE by roughly G (0.15%) on the next trading day
Heavy industries portfolio
Screening rules: |Z|>1.96, high confidence level
Daily maximum PM 2.5 index, 80% confidence level
Increase the probability of positive return of our portfolio by roughly G (0.58%) on the next trading day
SSE Composite Index Return
Screening rules: |Z|>1.96, high confidence level
Daily average NO2 , 85% confidence level
Increase the probability of positive return of SSE index by roughly G (0.53%)
1. Rapid development of heavy industries 2. One of the most important economic lifeline
Environmental Friendly Portfolio
Screening rules: |Z|>1.96, high confidence level
Daily average PM 10 index, 98% confidence level
Increase the probability of positive return of our portfolio by roughly G (0.98%) on the next trading day
Arouse the social concern on developing environmental friendly enterprise
• Air quality, especially PM 2.5 has a significant effect on stock returns
• The stock performance is affected by the air quality during the day or day before“3rd and 4th” quarter binary variable has significant effect on the stock performance
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