Final Project: Question Formation + Exploratory Data Analysis

Author

Anran Fang

Topic:

Focus on how may biotech companies’ approaches to sustainability affect their stock price. (Subject to edits.)

Data:

Currently referring to those in previous studies, including one that investigates impact of ESG index on stock return, one that researches on how does news affect biopharma stock prices, one that analyzes the impact of ESG on financial performances, and one that looks into US biopharmaceutical companies’ stock market reaction following a “paradoxical spiral”.

Hopefully these papers provide a scope of the companies that I want to look into, as well as their correlating sustainability data. Other financial data, if needed, would be cited from Yahoo Finance.

Data Analysis:

  1. Does efforts on sustainability improve their stock price? Is the effect significant? Is the effect short-term or long-term? What are some potential mechanism behind this effect? What are some side effects?

  2. Is the observed pattern specific to biotech companies, comparing to the other industries? Why? Will breaking biotech industry down to tech and pharmaceuticals company further clarifies or sharpens the relationship?

  3. What insightful conclusions can be drawn for investors?

Audience:

Including companies targeting biotech investments, biotech companies and their consulting agents that target a growth in stock price (or financial performance), and those ESG auditing companies creating frameworks and measurement for biotech industry.

Data Tidying:

Current two variables are stock price and ESG rating: Stock price from May 2018 to Apr 2024 will be recorded from Yahoo Finance on a monthly basis, which is the stock close price on the first day of each month. ESG rating will adopt MSCI ESG rating from 2019 to 2023. Assume each rating accurately captures the company’s ESG level of compliance until the next rating is published. The rating is given on a scale of AAA, AA, A, BBB, BB, B, CCC; For the ease of further analysis, the elements are each assigned a number, with AAA labeled 7 and CCC labeled 1.

Ten mega healthcare industry companies are chosen, with majority included in S&P 500. The criteria of screening is not only the business size, but more importantly that their MSCI ESG rating is available for MSCI only rated a limited number of mega companies.

The Tidied data is saved as a new csv file that can be directly imported for later analysis.

Data Analysis:

# Import the tidied data.
library(DT)

data <- read.csv(file = "/Users/far/Desktop/Felicia Far/JHU/Spring 2024/Sustainable Finance/Final Project/Clean Data.csv", header = TRUE, sep = ",", quote = "\"",
                    dec = ".", fill = TRUE, comment.char = "")

datatable(data, options = list(pageLength = 5))

Visualization 1

library(ggplot2)
library(dplyr)
library(lubridate)
library(scales)

data$Date <- as.Date(data$Date, format="%Y-%m-%d")

data_summary <- data %>%
  group_by(Month = floor_date(Date, "month")) %>%
  summarise(AverageStockPrice = mean(Stock.Price, na.rm = TRUE),
            AverageESGRating = mean(ESG.Rating, na.rm = TRUE))

p <- ggplot(data_summary, aes(x = Month)) + 
  geom_line(aes(y = AverageStockPrice, group = 1), color = "blue") +
  geom_line(aes(y = AverageESGRating*100, group = 1), color = "red") +
  scale_y_continuous(name = "Average Stock Price", sec.axis = sec_axis(~./100, name = "Average ESG Rating (Scaled)")) + 
  labs(title = "Trend of Average Stock Price and ESG Rating Over Time", x = "Date") +
  theme_minimal()

print(p)

A combined visualization of average stock prices and ESG ratings offers a snapshot of how sustainability measures might influence investor behavior and corporate valuation in the biotech sector. It allows for a direct, visual correlation between a company’s ESG commitment and its market performance over time. If both metrics show a positive trend, it could indicate that investors reward higher ESG scores with higher stock valuations, suggesting a financial incentive for biotech firms to adopt sustainable practices. Conversely, if higher ESG ratings do not correlate with stock performance, it may prompt a reevaluation of how sustainability efforts are communicated or executed. For stakeholders, this graph acts as a barometer for measuring the tangible benefits of sustainability in the marketplace, potentially guiding investment strategies and corporate governance. In an industry that is inherently future-focused, this visualization encapsulates the financial viability and ethical considerations of integrating ESG into business models.

Visualization 2

data$ESGCategory <- ifelse(data$ESG.Rating > median(data$ESG.Rating, na.rm = TRUE), "High", "Low")

data %>%
  filter(!is.na(ESG.Rating)) %>%
  group_by(Year = year(Date), ESGCategory) %>%
  summarise(AverageStockPrice = mean(Stock.Price, na.rm = TRUE)) %>%
  ggplot(aes(x = Year, y = AverageStockPrice, color = ESGCategory)) +
  geom_line() +
  labs(title = "Stock Price Trends by ESG Rating Category", x = "Year", y = "Average Stock Price")

Visualization 3

ggplot(data, aes(x = ESG.Rating, y = Stock.Price)) +
  geom_point() +
  geom_smooth(method = lm) +
  labs(title = "Correlation Between Stock Prices and ESG Ratings", x = "ESG Rating", y = "Stock Price")

The faceted line chart creates distinct visual segments for biotech companies categorized by high and low ESG ratings, offering a clear comparative analysis of stock price trends within these segments. This visualization is significant for discerning whether sustainable practices, as reflected by ESG ratings, have a differentiated impact on financial performance. It facilitates a side-by-side assessment that can highlight whether companies with higher sustainability standards perform better, worse, or similarly in the stock market compared to their lower-rated peers. By isolating each category, the chart eliminates the noise that could be present in a combined plot and allows for a focused analysis on the nuanced financial trajectory of each ESG tier. This targeted view can inform investors and stakeholders about the potential return on investment in sustainability and drive strategic decision-making within the biotech industry, centered on the integration of ESG considerations.

Visualization 4

data %>%
  group_by(Year = year(Date), ESGCategory) %>%
  summarise(AverageStockPrice = mean(Stock.Price, na.rm = TRUE)) %>%
  mutate(YearlyChange = AverageStockPrice - lag(AverageStockPrice)) %>%
  ggplot(aes(x = Year, y = YearlyChange, fill = ESGCategory)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Yearly Average Stock Price Change by ESG Rating Category", x = "Year", y = "Yearly Change in Average Stock Price")

By categorizing companies based on their ESG performance, it offers insights into how sustainability efforts influence stock price dynamics over time. A consistent pattern of positive stock price changes among companies with higher ESG ratings suggests that investors reward sustainability initiatives, signaling a market preference for socially responsible investments. Conversely, if stock price changes show little correlation with ESG ratings, it prompts a reevaluation of the effectiveness of current sustainability strategies and their perceived value in the market. This visualization informs investment decisions, corporate sustainability initiatives, and strategic planning, highlighting the evolving relationship between sustainability practices and financial performance in the biotech industry.