Sustainable Finance Week 3 Assignment Part II

library(tidyverse) 

-- Attaching packages --------------------------------------- tidyverse 1.3.1 --

v ggplot2 3.3.5     v purrr   0.3.4
v tibble  3.1.6     v dplyr   1.0.7
v tidyr   1.1.4     v stringr 1.4.0
v readr   2.1.1     v forcats 0.5.1

-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

etf_comparison_data_github_url <- "https://raw.githubusercontent.com/t-emery/sais-susfin_data/main/datasets/etf_comparison-2022-10-03.csv"

etf_comparison <- read_csv(etf_comparison_data_github_url)

Rows: 537 Columns: 14

-- Column specification --------------------------------------------------------
Delimiter: ","
chr (4): ticker, company_name, sector, esg_uw_ow
dbl (7): esg_etf, standard_etf, esg_tilt, esg_tilt_z_score, esg_tilt_rank, e...
lgl (3): in_esg_only, in_standard_only, in_on_index_only

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.

etf_comparison %>% 
  filter(standard_etf < 2) %>% 
  ggplot(aes(x = standard_etf, y = esg_etf)) +
  geom_point() +
  geom_smooth(method = lm)

`geom_smooth()` using formula 'y ~ x'

etf_comparison %>% 
  ggplot(aes(x = standard_etf, y = esg_etf)) +
  geom_point() +
  geom_smooth(method = lm)

`geom_smooth()` using formula 'y ~ x'

Here, we have a graph comparing the relationship between standard and esf etf’s. Here we see a strong positively correlated relationship between standard etf values and esg etf values. When removing outliers, the trend line did not change much. This makes sense because it shows that companies with high standard etf’s tend to also have high esg etf’s. This makes sense because the reputation of the company may be more significant than whether they have a standard or esg etf.

etf_comparison %>% 
  ggplot(aes(x = sector, y = esg_tilt)) +
  geom_point()

etf_comparison %>% 
  group_by(sector) %>% 
  mutate(mean_esg_tilt_sector = mean(esg_tilt)) %>% 
  ggplot(aes(x = sector, y = mean_esg_tilt_sector)) +
  geom_point()

This graph shows the different esg tilts by sector. Here it seems that esg tilt does not greatly by sector. Each sector has actors with low esg tilts and actors with high esg tilts. From this visual alone it is difficult to calculate which sector might have a higher average esg tilt. When actually calculating the mean by industry, the second graph shows that the information technology industry has the highest on average esg tilt while the communications industry has on average the lowest esg tilt. This is interesting because it shows what industries still need work regarding esg ambitions.

etf_comparison %>% 
  ggplot(aes( x = esg_tilt)) +
  geom_histogram() +
  stat_bin(bins = 100) +
 facet_wrap(~esg_uw_ow, ncol = 1)

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Here we can see a strong correlation between whether an esg fund is overweight and underweight and esg_tilt. Funds with a greater esg tilt were more likely to be overweight while funds with a smaller esg tilt were more likely to be underweight. This makes sense, but it is interesting that there is some overlap, meaning that esg tilt is not the only factor that determines whether or not a fund is overweight or underweight. This is important because it shows a correlation between whether a fund is overweight or underweight and its esg tilt, which can be used to better understand the impact and properties of esg tilts.