Deliverable, ValeriaLee

Quarto

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

Running Code

Q1

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.3     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(dplyr)
library(ggplot2)
options(scipen = 999)
blackrock_etf_screener <- read_csv("https://raw.githubusercontent.com/t-emery/sais-susfin_data/main/datasets/ishares_etf_screener_as_of_2023-12-27.csv") 

Rows: 424 Columns: 18
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (10): ticker, name, asset_class, sub_asset_class, region, market, locat...
dbl   (6): gross_expense_ratio_percent, net_expense_ratio_percent, net_asset...
dttm  (2): incept_date, net_assets_as_of

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

blackrock_etf_screener

# A tibble: 424 × 18
   ticker name                        incept_date         gross_expense_ratio_…¹
   <chr>  <chr>                       <dttm>                               <dbl>
 1 IVV    iShares Core S&P 500 ETF    2000-05-15 00:00:00                   0.03
 2 IEFA   iShares Core MSCI EAFE ETF  2012-10-18 00:00:00                   0.07
 3 AGG    iShares Core U.S. Aggregat… 2003-09-22 00:00:00                   0.03
 4 IWF    iShares Russell 1000 Growt… 2000-05-22 00:00:00                   0.19
 5 IJR    iShares Core S&P Small-Cap… 2000-05-22 00:00:00                   0.06
 6 IJH    iShares Core S&P Mid-Cap E… 2000-05-22 00:00:00                   0.05
 7 IEMG   iShares Core MSCI Emerging… 2012-10-18 00:00:00                   0.09
 8 IWM    iShares Russell 2000 ETF    2000-05-22 00:00:00                   0.19
 9 IWD    iShares Russell 1000 Value… 2000-05-22 00:00:00                   0.19
10 TLT    iShares 20+ Year Treasury … 2002-07-22 00:00:00                   0.15
# ℹ 414 more rows
# ℹ abbreviated name: ¹​gross_expense_ratio_percent
# ℹ 14 more variables: net_expense_ratio_percent <dbl>, net_assets_usd <dbl>,
#   net_assets_as_of <dttm>, asset_class <chr>, sub_asset_class <chr>,
#   region <chr>, market <chr>, location <chr>, investment_style <chr>,
#   msci_esg_fund_rating_aaa_ccc <chr>, msci_esg_quality_score_0_10 <dbl>,
#   msci_weighted_average_carbon_intensity_tons_co2e_m_sales <dbl>, …

blackrock_etf_screener |> 
  group_by(sustainable_classification) |> 
  summarize(
    n_funds = n(),
    assets_usd = sum(net_assets_usd, na.rm = TRUE)
    ) |> 
  arrange(assets_usd |> desc())

# A tibble: 5 × 3
  sustainable_classification n_funds assets_usd
  <chr>                        <int>      <dbl>
1 <NA>                           384    2.55e12
2 Uplift                          30    5.14e10
3 Thematic                         6    3.43e 9
4 Screened                         3    3.83e 8
5 Impact                           1    3.35e 8

blackrock_etf_screener |>
  filter(!is.na(sustainable_classification)) |>
  group_by(sustainable_classification) |>
  summarize(
    n = n(),
    percent_of_largest_fund = ( max(net_assets_usd) / sum(net_assets_usd) * 100)
  ) |>
  arrange(percent_of_largest_fund |> desc()) |>
  ungroup()

# A tibble: 4 × 3
  sustainable_classification     n percent_of_largest_fund
  <chr>                      <int>                   <dbl>
1 Impact                         1                   100  
2 Thematic                       6                    89.2
3 Screened                       3                    47.6
4 Uplift                        30                    26.0

Q2

library(ggplot2)
smallest_etfs <- blackrock_etf_screener |>
  filter(!is.na(net_assets_usd)) |>
  select(ticker, net_assets_usd) |>
  arrange(net_assets_usd) |>
  head(10)
smallest_etfs

# A tibble: 10 × 2
   ticker net_assets_usd
   <chr>           <dbl>
 1 ERUS          456626.
 2 AGIH         2470062.
 3 HYGI         2607469.
 4 IRTR         2711720.
 5 ITDA         2718433.
 6 ITDH         2739138.
 7 ITDI         2739138.
 8 ITDG         2739138.
 9 ITDF         2742012.
10 IWFH         3118795.

smallest_etfs <- blackrock_etf_screener |>
  arrange(net_assets_usd)|>
  head(10) |>
  mutate(ticker = fct_reorder(ticker, net_assets_usd))

ggplot(smallest_etfs, aes(x = ticker, y = net_assets_usd)) +
  geom_bar(width = 1, stat = "identity", fill = "grey40") +
  coord_flip() +
  labs(title = "Top 10 Smallest Etfs",
       x = "ETF Ticker",
       y = "Total Assets (in USD)")

theme_minimal()

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smallest_etfs |> 
  ggplot(aes(x = fct_reorder(ticker, net_assets_usd), y = net_assets_usd)) +
  geom_bar(width = 1, stat = "identity", fill = "grey40") +
  labs(title = "Top 10 Smallest ETFs",
       x = "ETF Ticker",
       y = "Total Assets (USD)")+
  theme_minimal()

Q3

bottom_score <- blackrock_etf_screener |>
  filter(
    msci_esg_quality_score_0_10 <= quantile
    (msci_esg_quality_score_0_10,0.25,
      na.rm = TRUE))
count(bottom_score)

# A tibble: 1 × 1
      n
  <int>
1   104

total_assest <- bottom_score |>
  group_by(asset_class, sub_asset_class) |>
  summarise(total_assest = sum(net_assets_usd, na.rm = TRUE))

`summarise()` has grouped output by 'asset_class'. You can override using the
`.groups` argument.

bottom_quintile <- blackrock_etf_screener |>
  filter(msci_esg_quality_score_0_10 <= quantile(msci_esg_quality_score_0_10, 0.25, 
                                                 na.rm = TRUE))
asset_classes <- bottom_quintile |>
  group_by(asset_class, sub_asset_class) %>%
  summarize(
    number_of_funds = n(),
    total_assets = sum(net_assets_usd),
    .groups = "drop"
  )
asset_classes

# A tibble: 10 × 4
   asset_class  sub_asset_class        number_of_funds  total_assets
   <chr>        <chr>                            <int>         <dbl>
 1 Equity       All Cap                             23  95486052602.
 2 Equity       Large Cap                            1   1852506812.
 3 Equity       Large/Mid Cap                       10  42421984678.
 4 Equity       Small Cap                           18 188148191582.
 5 Fixed Income Corporates                           1     41339182.
 6 Fixed Income Credit                               2   1994922916.
 7 Fixed Income Government                          24 220879023443.
 8 Fixed Income High Yield                          17  39783846631.
 9 Fixed Income Inflation                            4  27172554003.
10 Real Estate  Real Estate Securities               4   7145439523.

Q4

blackrock_etf_screener |>
  filter(!is.na(msci_esg_quality_score_0_10)) |>
  ggplot(aes(x = msci_esg_quality_score_0_10, y = market)) +
  geom_point(color = "grey") +
  labs(title = "Quality Score Comparison Between Markets",
       x = "Quality Score",
       y = "Market") +
  theme_minimal()

market_distribution <- bottom_score |>
  group_by(market) |>
  summarize(total_weight = n())

distribution <- ggplot(market_distribution, aes(x = "", y = total_weight, fill = market)) +
  geom_bar(width = 1.0, stat = "identity") +
  labs(title = "Bottom Quantile Market Score Distribution",
       x = "Score Distribution",
       y = "Market") +
  coord_polar("y", start = 0) + 
  theme_void()

print(distribution)

ggplot(blackrock_etf_screener, aes(x = msci_esg_fund_rating_aaa_ccc, y = region, fill = region)) +
  geom_boxplot() +
  labs(title = "Rating Distribution in Each Region",
       x = "Rating",
       y = "Region",
       fill = "Region") +
  theme_minimal()

ggplot(blackrock_etf_screener, aes(x = msci_esg_fund_rating_aaa_ccc, fill = market)) +
  geom_bar() +
  labs(
    title = "Rating Distribution by Markets",
    x = "Rating",
    y = "Amount of Funds",
    fill = "Market"
  ) +
  theme_minimal()

smallest_gross_expense_etfs <- blackrock_etf_screener %>%
  arrange(gross_expense_ratio_percent) %>%
  head(5) %>%
  mutate(ticker = fct_reorder(ticker, net_assets_usd))

ggplot(smallest_gross_expense_etfs, aes(x = ticker, y = net_assets_usd)) +
  geom_bar(stat = "identity", fill = "grey40") +
  coord_flip() +
  labs(title = "Top 5 Smallest Gross Expense Etfs",
       x = "ETF Ticker",
       y = "Total Assets (in USD)")

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