Weathering the Storm

Timeseries comparison and the impact of Covid-19 on the financial markets by sector

In 2020, Covid-19 began battering financial markets now further impacted by the war in Ukraine. Which sectors are faring best?

I’ll compare each sector in the S&P 500 with the overall market. Baselining each at zero as of February 19th, we’ll see which were the first to recover lost ground.

library(conflicted)
library(tidyverse)
conflict_prefer_all("dplyr")
library(wesanderson)
library(scales)
library(glue)
library(tidyquant)
library(clock)
conflict_prefer("date_format", "clock")
library(usedthese)

conflict_scout()

9 conflicts:
* `as_date`    : clock, lubridate
* `col_factor` : scales, readr
* `date_format`: [clock]
* `discard`    : scales, purrr
* `filter`     : [dplyr]
* `first`      : [dplyr]
* `lag`        : [dplyr]
* `last`       : [dplyr]
* `legend`     : PerformanceAnalytics, graphics

theme_set(theme_bw())

(cols <- wes_palette("Moonrise2"))

symbols <-
  c(
    "SPY",
    "XLV",
    "XLK",
    "XLE",
    "XLF",
    "XLC",
    "XLI",
    "XLY",
    "XLP",
    "XLRE",
    "XLU",
    "XLB"
  )

from <- "2020-02-19"

from_formatted <- date_parse(from, format = "%Y-%m-%d") |> 
  date_format(format = "%b %d, %Y")

eod_sectors <-
  tq_get(symbols, from = from) |>
  mutate(
    norm_close = adjusted / first(adjusted) - 1,
    type = if_else(symbol == "SPY", "Market", "Sector"),
    sector = case_when(
      symbol == "SPY"  ~ "S&P 500",
      symbol == "XLB"  ~ "Materials",
      symbol == "XLE"  ~ "Energy",
      symbol == "XLU"  ~ "Utilities",
      symbol == "XLI"  ~ "Industrical",
      symbol == "XLRE" ~ "Real Estate",
      symbol == "XLV"  ~ "Health",
      symbol == "XLK"  ~ "Technology",
      symbol == "XLF"  ~ "Financial",
      symbol == "XLC"  ~ "Communication",
      symbol == "XLY"  ~ "Consumer Discretionary",
      symbol == "XLP"  ~ "Consumer Staples",
      TRUE             ~ "Other"
    ), .by = symbol
  ) |>
  drop_na()

Perhaps not too surprising to see that Tech led the way back from Covid. But with the further impact of the situation in Ukraine, the Energy sector is now the strongest performer relative to February 2020. Comms, with all that home-working, benefited initially during the lockdown, but has faded since.

eod_sectors |>
  mutate(
    sector = str_wrap(sector, 12),
    sector = fct_reorder(sector, norm_close, last, .desc = TRUE)
  ) |>
  ggplot(aes(date, norm_close, colour = sign(norm_close))) +
  geom_hline(yintercept = 0, linetype = "dashed", colour = "grey80") +
  geom_line() +
  facet_wrap(~sector) +
  scale_colour_gradient(low = cols[2], high = cols[1]) +
  scale_y_continuous(labels = label_percent()) +
  labs(
    title = "S&P 500 Sector Impact of Covid-19 & Ukraine",
    subtitle = glue("Relative to {from_formatted}"),
    x = NULL, y = NULL, colour = NULL
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.position = "none")

R Toolbox

Summarising below the packages and functions used in this post enables me to separately create a toolbox visualisation summarising the usage of packages and functions across all posts.

used_here()

Package	Function
base	c[1], library[8], sign[1]
clock	date_format[1], date_parse[1]
conflicted	conflict_prefer[1], conflict_prefer_all[1], conflict_scout[1]
dplyr	case_when[1], first[1], if_else[1], mutate[2]
forcats	fct_reorder[1]
ggplot2	aes[1], element_text[1], facet_wrap[1], geom_hline[1], geom_line[1], ggplot[1], labs[1], scale_colour_gradient[1], scale_y_continuous[1], theme[1], theme_bw[1], theme_set[1]
glue	glue[1]
scales	label_percent[1]
stringr	str_wrap[1]
tidyquant	tq_get[1]
tidyr	drop_na[1]
usedthese	used_here[1]
wesanderson	wes_palette[1]