BlackRock ESG ETF Data

Analysis of BlackRock ESG ETF Data

library(tidyverse) 

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0     ✔ purrr   1.0.1
✔ tibble  3.1.8     ✔ dplyr   1.1.0
✔ tidyr   1.3.0     ✔ stringr 1.5.0
✔ readr   2.1.3     ✔ forcats 1.0.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

github_raw_csv_url <- "https://raw.githubusercontent.com/t-emery/sais-susfin_data/main/datasets/blackrock_etf_screener_2022-08-30.csv"


blackrock_etf_data <- read_csv(github_raw_csv_url)

Rows: 393 Columns: 22
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (14): ticker, name, incept_date, net_assets_as_of, asset_class, sub_asse...
dbl  (8): gross_expense_ratio_percent, net_expense_ratio_percent, net_assets...

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_data <- blackrock_etf_data |> 
  # we are transforming both date columns (currently character strings) into date objects
  # so we can work with them.
  # this syntax is a bit confusing, but selects all columns containing `date` and applies
  # lubridate::mdy() function to them to turn them into date objects. 
  mutate(across(contains("date"), lubridate::mdy)) |>
  # Billions is a more useful magnitude than millions, so we'll create a column with 
  # the assets in billions by dividing by `net_assets_millions` by 1,000 (10^3)
  # If we wanted trillions, we could divide by 1,000,000 (10^6)
  mutate(net_assets_usd_bn = net_assets_usd_mn/10^3) |> 
  # this column doesn't add anything to our analysis - it says that the data is from 8/30/22
  select(-net_assets_as_of)

blackrock_etf_data <- blackrock_etf_data |> 
  rename(co2_intensity = msci_weighted_average_carbon_intensity_tons_co2e_m_sales)

Year of net assets launched

glimpse(blackrock_etf_data)

Rows: 393
Columns: 22
$ ticker                                                                            <chr> …
$ name                                                                              <chr> …
$ incept_date                                                                       <date> …
$ gross_expense_ratio_percent                                                       <dbl> …
$ net_expense_ratio_percent                                                         <dbl> …
$ net_assets_usd_mn                                                                 <dbl> …
$ asset_class                                                                       <chr> …
$ sub_asset_class                                                                   <chr> …
$ region                                                                            <chr> …
$ market                                                                            <chr> …
$ location                                                                          <chr> …
$ investment_style                                                                  <chr> …
$ sustainability_characteristics_msci_esg_fund_ratings_msci_esg_fund_rating_aaa_ccc <chr> …
$ msci_esg_quality_score_0_10                                                       <dbl> …
$ co2_intensity                                                                     <dbl> …
$ msci_esg_percent_coverage                                                         <dbl> …
$ sustainable_classification                                                        <chr> …
$ name_wo_ishares_etf                                                               <chr> …
$ is_esg                                                                            <chr> …
$ years_from_inception                                                              <dbl> …
$ year_launched                                                                     <dbl> …
$ net_assets_usd_bn                                                                 <dbl> …

a<-blackrock_etf_data|>group_by(year_launched)
b<-summarize(a,year_launched, net_assets_usd_bn=sum(net_assets_usd_bn))

Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
dplyr 1.1.0.
ℹ Please use `reframe()` instead.
ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
  always returns an ungrouped data frame and adjust accordingly.

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

ggplot(data=b, mapping=aes(x=year_launched, y=net_assets_usd_bn))+geom_smooth(se=FALSE)

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

We discover that from 1995 to 2020, the value of net assets of ESG fund launched were lower and lower. This is a little bit strange as the value of net assets should be higher over time.

c<-summarize(a,count=n(),net_assets=sum(net_assets_usd_bn))
ggplot(data=c, mapping=aes(x=year_launched,y=net_assets))+geom_point(aes(size=count),alpha=1/3)+geom_smooth(se=FALSE)

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

With a closer look at the relationship with the amount of fund launched and the time launched, we found that in recent years, there were indeed more ESG Fund launched, but their unit value was decreasing.

d<-summarize(a,count=n(),net_assets=sum(net_assets_usd_bn))
ggplot(data=d,mapping=aes(x=year_launched,y=net_assets))+geom_smooth(aes(net_assets),se=FALSE)

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

However, in this chart we notice that over year the value of net assets launched increased.