Final(RMarkdown V)

Comparative Analysis of ESG ETFs and Thematic ETFs

Yixiao Josie Zhou

April 2023

Introduction:

This research proposal seeks to explore the comparative performance and risk profiles of Environmental, Social, and Governance (ESG) Exchange-Traded Funds (ETFs) against other ESG-related thematic ETFs, including those focused on clean energy, natural resources, and electric vehicles and batteries. With a growing interest in sustainable finance and thematic investing, particularly in the arenas of energy transition and climate solutions, this study aims to dissect investor behaviors and preferences, providing a nuanced understanding of the financial dynamics at play. Utilizing historical financial data sourced from respected databases like Bloomberg, Morningstar, and Yahoo Finance, alongside ESG ratings and sustainability metrics, this project will analyze a range of factors from risk-adjusted returns to sector allocations, offering valuable insights to both investors and financial professionals focused on sustainable and thematic investment strategies.

ETF Overview

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library(openxlsx)
library(leaflet)
library(dplyr)
library(htmltools)
library(rnaturalearth)
library(scales)

data <- read.xlsx("/Users/hahazhou/Desktop/Final Project/00_data_raw/country.xlsx")

data <- data %>%
  rename(Country = `Country`, Netflow = `Netflow`, iso3c = `iso3c`) %>%
  mutate(Netflow = as.numeric(Netflow))

world <- ne_countries(scale = "medium", returnclass = "sf")

world_data <- world %>%
  left_join(data, by = c("iso_a3" = "iso3c"))

# Set the breaks manually based on your data distribution or use quantiles
breaks <- quantile(world_data$Netflow, probs = seq(0, 1, length.out = 10), na.rm = TRUE)
qpal <- colorBin(palette = rev(colorRampPalette(colors = c("olivedrab", "coral"))(10)), 
                 bins = breaks)

labels <- sprintf(
  "<strong>%s</strong><br/>Netflow: %s",
  world_data$name, world_data$Netflow
) %>% lapply(htmltools::HTML)

leaflet(world_data) %>%
  addTiles() %>%
  addPolygons(fillColor = ~qpal(Netflow),
              weight = 0.5,
              opacity = 1,
              color = "white",
              dashArray = "3",
              fillOpacity = 0.7,
              highlight = highlightOptions(weight = 5,
                                           color = "#666",
                                           dashArray = "3",
                                           fillOpacity = 0.7,
                                           bringToFront = TRUE),
              label = labels,
              labelOptions = labelOptions(style = list("font-weight" = "normal", padding = "1.5px 4px"),
                                          textsize = "6px",
                                          direction = "auto")) %>%
  addLegend(pal = qpal, values = ~Netflow, opacity = 0.7, 
            title = "Netflow of ETFs (M USD)", position = "bottomright")

AUM

The analysis of asset management data reveals distinct trends in the asset under management (AUM) for various ETFs, particularly highlighting the performance of ESG (Environmental, Social, and Governance) ETFs compared to thematic ETFs. Notably, the AUM for ESG ETFs remains substantially higher than that of thematic ETFs, such as those focused on specific sectors like clean energy or technology. This is despite a noticeable slowdown in growth since 2022, where ESG ETFs have seen a plateau in their expansion. The enduring size advantage of ESG ETFs underscores the robust demand from both individual and institutional investors, who are increasingly seeking to bolster their portfolios with investments that align with sustainable and responsible principles.

The substantial lead in AUM held by ESG ETFs over thematic ETFs can be attributed primarily to the growing appetite for ESG-focused investments. This shift is driven by the broader market realization of the importance of sustainability criteria in investment decision-making, reflecting a deep-rooted shift towards responsible investing. Institutional investors, in particular, have been pivotal in this trend, as they continue to increase their exposure to ESG strategies to meet both internal mandates and external expectations for sustainability.

However, the recent stagnation in AUM growth for ESG ETFs may also reflect emerging challenges within the ESG investing landscape. A backlash against ESG investing principles in certain markets and among specific investor segments has introduced some volatility and hesitation, potentially slowing the flow of new capital into these funds. This backlash could be linked to debates over the efficacy and impact of ESG metrics and the perception of ESG investing as a politically charged or overly complex field.

In conclusion, while the AUM of ESG ETFs indicates a strong foundational interest and a significant lead over thematic ETFs, the dynamics of investor sentiment and market trends will be crucial in shaping their trajectory moving forward. The ability of ESG funds to navigate the complexities of market expectations and regulatory landscapes will likely determine their future growth and influence in the broader asset management industry.

# Import required libraries
library(readxl)
library(dplyr)
library(tidyr)

# Read data
AUM_ESG <- read_excel("/Users/hahazhou/Desktop/Final Project/00_data_raw/AUM_ESG.xlsx")
AUM_Thematic <- read_excel("/Users/hahazhou/Desktop/Final Project/00_data_raw/AUM_Thematic ETF.xlsx")

# Add "Type" column and fill values
AUM_ESG <- AUM_ESG %>% mutate(Type = "ESG")
AUM_Thematic <- AUM_Thematic %>% mutate(Type = "Thematic")

# Combine datasets
Combined_Volume <- bind_rows(AUM_ESG, AUM_Thematic)

# Handle duplicates
Combined_Volume <- Combined_Volume %>%
  group_by(Ticker) %>%
  mutate(duplicate = n() > 1) %>%
  ungroup() %>%
  filter(!duplicate | Type == "ESG") %>%
  mutate(Type = ifelse(duplicate, "ESG and Thematic", Type)) %>%
  select(-duplicate)

# Keep only the first 4 letters in the Ticker column
Combined_Volume$Ticker <- substr(Combined_Volume$Ticker, 1, 4)

# Transform to long format
Combined_Volume_long <- Combined_Volume %>%
  pivot_longer(cols = starts_with("20"), names_to = "Quarter", values_to = "AUM")

library(plotly)
library(ggplot2)
library(dplyr)

library(plotly)
a <- ggplot(Combined_Volume_long) +
  aes(x = Quarter, y = AUM, fill = Ticker) +
  geom_col() +
  scale_fill_hue(direction = 1) +
  labs(
    x = "Quarter",
    y = "AUM",
    title = "AUM for ETFs by Type and Ticker",
    caption = "Data Source: Bloomberg"
  ) +
  theme_minimal() +
  facet_wrap(vars(Type)) +
  guides(fill = guide_none()) + # This removes the legend for the 'fill' aesthetic
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 5))


# Converting the ggplot object to a plotly object
ggplotly(a)

# Read data
Volume_ESG <- read_excel("/Users/hahazhou/Desktop/Final Project/00_data_raw/Volume_ESG.xlsx")
Volume_Thematic <- read_excel("/Users/hahazhou/Desktop/Final Project/00_data_raw/Volume_Thematic ETF.xlsx")

# Add "Type" column and fill values
Volume_ESG <- Volume_ESG %>% mutate(Type = "ESG")
Volume_Thematic <- Volume_Thematic %>% mutate(Type = "Thematic")

# Combine datasets
Combined_V <- bind_rows(Volume_ESG, Volume_Thematic)

# Handle duplicates
Combined_V <- Combined_V %>%
  group_by(Ticker) %>%
  mutate(duplicate = n() > 1) %>%
  ungroup() %>%
  filter(!duplicate | Type == "ESG") %>%
  mutate(Type = ifelse(duplicate, "ESG and Thematic", Type)) %>%
  select(-duplicate)

# Keep only the first 4 letters in the Ticker column
Combined_V$Ticker <- substr(Combined_V$Ticker, 1, 4)

# Transform to long format
Combined_V_long <- Combined_V %>%
  pivot_longer(cols = starts_with("20"), names_to = "Quarter", values_to = "$ Volume")

Trading Volumn

The trading volume data for both ESG and thematic ETFs exhibit similar trends, with each category reaching its peak around 2021 before experiencing a decline. This synchronicity suggests that external factors, likely linked to the broader market environment, have influenced trading activities across different ETF categories. The subsequent decrease in trading volumes may be attributed to the backlash against ESG investing principles, which has also affected thematic ETFs that are often intertwined with ESG themes. Such a downturn reflects investor hesitancy and a reassessment of risk associated with the perceived politicization and regulatory scrutiny of ESG investments. Despite these challenges, the initial high volumes indicate a strong past investor engagement and suggest that underlying interest in specialized ETFs remains, albeit tempered by recent market dynamics.

b <- ggplot(Combined_V_long) +
  aes(x = Quarter, y = `$ Volume`, fill = Ticker) +
  geom_col() +
  scale_fill_hue(direction = 1) +
  labs(
    x = "Quarter",
    y = "$ Volume",
    title = "$ Volume by Type and Ticker",
    caption = "Data source: Bloomberg"
  ) +
  theme_minimal() +
  facet_wrap(vars(Type)) +
  guides(fill = guide_none()) +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 5))
ggplotly(b)

# Read data
FF_ESG <- read_excel("/Users/hahazhou/Desktop/Final Project/00_data_raw/Fund Flow_ESG.xlsx")
FF_Thematic <- read_excel("/Users/hahazhou/Desktop/Final Project/00_data_raw/Fund Flow_Thematic ETF.xlsx")

# Add "Type" column and fill values
FF_ESG <- FF_ESG %>% mutate(Type = "ESG")
FF_Thematic <- FF_Thematic %>% mutate(Type = "Thematic")

# Combine datasets
Combined_FF <- bind_rows(FF_ESG, FF_Thematic)

# Handle duplicates
Combined_FF <- Combined_FF %>%
  group_by(Ticker) %>%
  mutate(duplicate = n() > 1) %>%
  ungroup() %>%
  filter(!duplicate | Type == "ESG") %>%
  mutate(Type = ifelse(duplicate, "ESG and Thematic", Type)) %>%
  select(-duplicate)

# Keep only the first 4 letters in the Ticker column
Combined_FF$Ticker <- substr(Combined_FF$Ticker, 1, 4)

# Transform to long format
Combined_FF_long <- Combined_FF %>%
  pivot_longer(cols = starts_with("20"), names_to = "Quarter", values_to = "Fund Flow")

# Replace NA with 0
Combined_FF_long$`Fund Flow`[is.na(Combined_FF_long$`Fund Flow`)] <- 0

Fund Flow

The fund flow dynamics between ESG ETFs and thematic ETFs related to environmentally sustainable topics such as clean energy, natural resources, and electric vehicles reveal contrasting trends. Notably, ESG ETFs have experienced a significant outflow, which peaked in the first quarter of 2023. In stark contrast, thematic ETFs have not encountered similar large-scale withdrawals. This divergence in fund flows suggests that the outflows from ESG ETFs are more reflective of shifting political and social attitudes towards ESG principles rather than a diminished interest in environmental sustainability as an investment target. The relatively stable fund flows in thematic ETFs underscore that investor interest in specific environmental themes remains robust. This pattern indicates that the backlash affecting ESG funds may be rooted in the broader interpretation and implementation of ESG criteria, rather than a rejection of the underlying value of environmentally focused investments.

c <- ggplot(Combined_FF_long) +
  aes(x = Quarter, y = `Fund Flow`, fill = Ticker) +
  geom_col() +
  scale_fill_hue(direction = 1) +
  labs(
    x = "Quarter",
    y = "Fund Flow",
    title = "Fund Flow by Type and Ticker",
    caption = "Data source: Bloomberg"
  ) +
  theme_minimal() +
  facet_wrap(vars(Type)) +
  guides(fill = guide_none()) +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 5))
ggplotly(c)

library(tidyquant)
library(dplyr)

Thematic_tickers <- read.csv("/Users/hahazhou/Desktop/Final Project/03_data_processed/Volume_Thematic_Cleaned.csv") %>%
  distinct(Ticker)

ESG_tickers <- read.csv("/Users/hahazhou/Desktop/Final Project/03_data_processed/Volume_ESG_Cleaned.csv") %>%
  distinct(Ticker)

Combined_tickers <- full_join(
  mutate(Thematic_tickers, Type = "Thematic"),
  mutate(ESG_tickers, Type = "ESG"),
  by = "Ticker"
) %>%
  mutate(Type = ifelse(is.na(Type.x) & is.na(Type.y), NA,
                       ifelse(!is.na(Type.x) & !is.na(Type.y), "ESG and Thematic",
                              coalesce(Type.x, Type.y)))) %>%
  select(Ticker, Type)

combined_returns <- Combined_tickers %>%
  tq_get(get = "stock.prices",
         from = "2018-01-01",
         collapse = "monthly") %>%
  group_by(Ticker, Type) %>%
  tq_transmute(select = adjusted,
               mutate_fun = periodReturn,
               period = "monthly",
               col_rename = "monthly.returns")

Monthly Return

Analysis of the monthly returns for ESG ETFs compared to thematic ETFs reveals a distinct pattern of higher volatility in ESG funds. While the specific causes of this heightened volatility remain to be fully explored, preliminary insights suggest that ESG ETFs may be more sensitive to market dynamics and investor sentiment, which can be influenced by a rapidly evolving regulatory and geopolitical landscape. This variability in returns contrasts with the somewhat steadier performance observed in thematic ETFs focused on specific sectors like clean energy, natural resources, and electric vehicles. These thematic ETFs typically exhibit lower volatility, possibly due to their more focused investment strategies and the specific nature of their targeted industries. The comprehensive investigation planned for the final research report will aim to delve deeper into the underlying factors contributing to the volatility in ESG ETFs, providing a more nuanced understanding of the risks and rewards associated with these investment vehicles.

library(ggplot2)
library(plotly)
library(dplyr)

# Creating the ggplot object with the required aesthetics and settings
p <- ggplot(combined_returns, aes(x = date, y = monthly.returns, colour = Ticker)) +
  geom_line() +
  scale_color_viridis_d(option = "viridis", direction = 1) +  # Using a Viridis color scale
  labs(
    x = "Year",
    y = "Monthly Returns",
    title = "Monthly Returns by Type and Ticker",
    caption = "Data Source: Bloomberg"
  ) +
  theme_gray() +
  facet_wrap(vars(Type)) +
  guides(colour = guide_none())  # This removes the legend for the 'colour' aesthetic

# Converting the ggplot object to a plotly object for interactivity
ggplotly(p)

Conclusion

In conclusion, the comparative analysis of ESG and thematic ETFs underscores a complex landscape of investor behavior and market dynamics. ESG ETFs, while still holding a significant advantage in terms of asset under management, have faced challenges with a marked slowdown in growth and significant fund outflows, primarily influenced by shifting political and social attitudes. On the other hand, thematic ETFs related to environmental sustainability have maintained stable fund flows, affirming continued investor interest in specific environmental sectors. Additionally, the volatility observed in the monthly returns of ESG ETFs highlights their sensitivity to external pressures, which merits further investigation. This research has illuminated the nuanced reactions of these investment vehicles to external influences and internal characteristics, offering a foundation for deeper exploration into the factors driving these trends in the final research report.

References

To be filled