Initial findings + data EDA

Make a Plan

In today’s society, more and more investors are beginning to pay attention to the environmental, social and governance (ESG) performance of enterprises, and the evaluation and analysis of ESG factors has become an indispensable part of investment decisions.

Sustainable finance generally refers to the process of taking appropriate account of environmental (E), social (S) and governance (G) factors in financial sector investment decisions, thereby increasing long-term investment in sustainable economic activities and projects.ESG emphasizes the benefits to the environment and society, as well as the improvement of governance systems. As a cutting-edge concept to effectively balance the needs of high-quality economic, environmental and social development in the era of new economic development, ESG theory and its application system can provide a framework and path reference for enterprises to achieve their own high-quality development, practice responsible investment, and promote low-carbon transformation, and is also a wind vane of the global capital market. This project aims to explore the relationship between ESG ratings and stock market performance using R language and collected data. This report will examine the correlation between a stock’s ESG score and its annual return, and delve into the impact of environmental, social and governance scores on stock market performance.

Data Preparation

library(readxl)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(ggplot2)

df <- read_excel("~/Final Project Data/00_data_raw/ESG_score.xlsx")
df$stock <- gsub('.*(\\d{6}).*', '\\1', df$stock)
write.csv(df, file = "~/Final Project Data/01_data_cleaning/ESG_score.csv", row.names = FALSE)

stock <- read_excel("~/Final Project Data/00_data_raw/stock.xlsx")
stock$stock <- sprintf("%06d", as.numeric(stock$stock))
write.csv(stock, file = "~/Final Project Data/01_data_cleaning/stock.csv", row.names = FALSE)

df2 <- read.csv('~/Final Project Data/01_data_cleaning/ESG_score.csv')
df1 <- read.csv('~/Final Project Data/01_data_cleaning/stock.csv')
merge <- merge(df1, df2, by = c('stock', 'year'), all.x = TRUE)
merge <- na.omit(merge)

write.csv(merge, file = '~/Final Project Data/stock_esg.csv', row.names = FALSE)