DANL210 Project

Stock Performance vs Ethical Performance

Author

Ann Brennan

1 Introduction

This project will address the relationship between Environmental, Governance, and Social (ESG) risk ratings and stock performance. ESG risk ratings evaluate a company’s ethical performance and how well they manage risks and opportunities in environmental, social, and governance areas. Given this, I will be able to analyze the relationship between stock performance and ethical performance. All data was collected from Yahoo Finance.

2 Data

import numpy as np
import pandas as pd
import seaborn as sns
import yfinance as yf

tickers_history = pd.read_csv("tickers-hist.csv")
esg_data = pd.read_csv("esg.csv")

esg_data = esg_data.rename(columns={'0': 'total-esg', '0.1': 'enviro_risk', '0.2':'social_risk', '0.3':'gov_risk', '0.4':'controversy'})

2.1 Variable Description

2.1.1 esg_data

total_esg: Total ESG rating. Environmental, social, and government risk ratings combined.
enviro_risk: Environmental risk rating.
social_risk: Social risk rating.
gov_risk: Governmental risk rating.
controversy: Controversy level.

3 Descriptive Statistics

3.1 Summary Statistics

esg_data['enviro_risk'].describe()

count    537.000000
mean       5.827188
std        5.301806
min        0.000000
25%        1.800000
50%        4.000000
75%        9.000000
max       27.300000
Name: enviro_risk, dtype: float64

esg_data['social_risk'].describe()

count    537.000000
mean       9.042644
std        3.612652
min        0.800000
25%        6.700000
50%        8.900000
75%       11.100000
max       22.500000
Name: social_risk, dtype: float64

esg_data['gov_risk'].describe()

count    537.000000
mean       6.811173
std        2.378037
min        2.400000
25%        5.200000
50%        6.300000
75%        7.900000
max       19.400000
Name: gov_risk, dtype: float64

tickers_history['Close'].describe()

count    357202.000000
mean        142.152567
std         297.241789
min           0.980000
25%          40.449798
50%          81.209999
75%         152.080002
max        8099.959961
Name: Close, dtype: float64

tickers_history['High'].describe()

count    357202.000000
mean        143.808907
std         300.607248
min           1.060000
25%          40.966236
50%          82.190002
75%         153.830002
max        8158.990234
Name: High, dtype: float64

tickers_history['Low'].describe()

count    357202.000000
mean        140.424490
std         293.761505
min           0.780000
25%          39.901238
50%          80.220911
75%         150.270004
max        8010.000000
Name: Low, dtype: float64

Merge the data frames esg_data and tickers_history in order to compare:

merged_df = pd.merge(esg_data, tickers_history, left_index = True, right_index = True, how = 'inner')

Distribution of Close:

sns.histplot(merged_df['Close'])

Distribution of Environmental Risk Rating:

sns.histplot(merged_df['enviro_risk'])

4 Exploratory Data Analysis

4.1 Correlation Heat Map of ESG Data:

import matplotlib.pyplot as plt
corr = esg_data.corr()
plt.figure(figsize=(8,6))
sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f', linewidths = .5)
plt.title('Correlation Heatmap of ESG Data')
plt.show()

4.2 Relationship between Close Price and Environmental Risk Score:

sns.lmplot(merged_df, x = 'enviro_risk', y = 'Close')

There is not much correlation between Close Price and Environmental Risk Score. Our scatter plot and best fit line are relatively flat. If anything, there is a slight positive relationship. Higher risk might mean a higher close price, however, we cannot make a conclusion from this.

4.3 Next, relationship between Close Price and Social Risk Score:

sns.lmplot(merged_df, x = 'social_risk', y = 'Close')

Again, there is not much correlation between Close Price and Social Risk Score. Based on the graph, there could be a slight negative relationship, if any. Higher risk could mean lower close price. However, we cannot conclude this.

4.4 Finally, relationship between Close Price and Government Risk Score:

sns.lmplot(merged_df, x = 'gov_risk', y = 'Close')

Once again, there is not much correlation between the two. Like environmental risk, there could be a slight positive relationship. As risk increases, so does close price. However, we cannot conclude this.

5 Conclusion

There may be somewhat of a relationship between stock performance, such as a company’s Close Price, and ethical performance. However, more research is needed. A conclusion can not be reached simply from Yahoo Finance. There are also other factors besides risk ratings that affect stock performance. Examples include macroeconomic conditions, industry trends, and company financials.

6 Significance

It is important for companies to pay attention to their own ethical and stock performance and how the two interact. Companies that perform well in ESG ratings may be able to achieve long term sustainability in their business. Long term sustainability may in turn attract new investors and minimize risk. In addition, they can use ESG ratings to see how well they are handling risks and opportunities. This may give companies a competitive edge.