options(repos = c(CRAN = "https://cloud.r-project.org"))

Goal - To analyze the influence of tariffs on popular stocks within different sectors.

Note: Choices of stocks reflect popular stocks among the Swedish population.

The choosen stocks are: Atlas Copco (Industrial), Investor (Financials), SINCH (Information Technology), Orrön (Energy), SSAB (Material), SAAB (Defense), Axfood (Consumer Staples), Volvo Car (Consumer Discretionary), AstraZeneca (Healtcare), Nordea (Bank), Ericsson (Communication Services), Castellum (Real Estate)

Stock Price

The Stock price is collected from March 1st, a month ahead of liberation day to May 10th.

stocks <- tq_get(c("ATCO-A.ST", "SINCH.ST", "INVE-B.st", "SSAB-B.ST", "SAAB-B.ST", "AXFO.ST", "VOLCAR-B.ST", "NDA-SE.ST", "ERIC-B.ST", "CAST.ST", "ORRON.ST", "AZN.ST"),
                 get = "stock.prices",
                 from = "2025-03-01",
                 to = "2025-05-10")
stocks
## # A tibble: 564 × 8
##    symbol    date        open  high   low close  volume adjusted
##    <chr>     <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>
##  1 ATCO-A.ST 2025-03-03  183.  184.  181.  181. 5851286     179.
##  2 ATCO-A.ST 2025-03-04  178.  179.  174.  175. 5849265     174.
##  3 ATCO-A.ST 2025-03-05  180.  185.  178.  184. 6462212     183.
##  4 ATCO-A.ST 2025-03-06  186.  186.  179.  183. 5369088     181.
##  5 ATCO-A.ST 2025-03-07  181.  184.  179.  183. 6084395     182.
##  6 ATCO-A.ST 2025-03-10  184.  185.  179.  179. 3915631     177.
##  7 ATCO-A.ST 2025-03-11  182.  182.  175.  175. 4600879     173.
##  8 ATCO-A.ST 2025-03-12  178.  179.  176.  177. 6365358     175.
##  9 ATCO-A.ST 2025-03-13  175.  178.  174.  176. 3017065     174.
## 10 ATCO-A.ST 2025-03-14  175.  177.  175.  177. 3440299     175.
## # ℹ 554 more rows

Stock Prices Graphed

stocks %>%
    
    ggplot(aes(x = date, y = adjusted, color = symbol)) +
    geom_line()

Improved Graph

library(yfR)
library(ggplot2)
library(dplyr)
library(scales)
## 
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
## 
##     col_factor
# Load data
tickers <- c("ATCO-A.ST", "SINCH.ST", "INVE-B.st", "SSAB-B.ST", "SAAB-B.ST", "AXFO.ST", "VOLCAR-B.ST", "NDA-SE.ST", "ERIC-B.ST", "CAST.ST", "ORRON.ST", "AZN.ST")

df_stocks <- yf_get(tickers = tickers, 
                        first_date = as.Date("2025-03-01"), 
                        last_date = as.Date("2025-05-10"))
## 
## ── Running yfR for 12 stocks | 2025-03-01 --> 2025-05-10 (70 days) ──
## 
## ℹ Downloading data for benchmark ticker ^GSPC
## ℹ (1/12) Fetching data for ATCO-A.ST
## !    - not cached
## ✔    - cache saved successfully
## ✔    - got 47 valid rows (2025-03-03 --> 2025-05-09)
## ✔    - got 96% of valid prices -- Nice!
## ℹ (2/12) Fetching data for AXFO.ST
## !    - not cached
## ✔    - cache saved successfully
## ✔    - got 47 valid rows (2025-03-03 --> 2025-05-09)
## ✔    - got 96% of valid prices -- Good job vincentsmac!
## ℹ (3/12) Fetching data for AZN.ST
## !    - not cached
## ✔    - cache saved successfully
## ✔    - got 47 valid rows (2025-03-03 --> 2025-05-09)
## ✔    - got 96% of valid prices -- Time for some tea?
## ℹ (4/12) Fetching data for CAST.ST
## !    - not cached
## ✔    - cache saved successfully
## ✔    - got 47 valid rows (2025-03-03 --> 2025-05-09)
## ✔    - got 96% of valid prices -- Youre doing good!
## ℹ (5/12) Fetching data for ERIC-B.ST
## !    - not cached
## ✔    - cache saved successfully
## ✔    - got 47 valid rows (2025-03-03 --> 2025-05-09)
## ✔    - got 96% of valid prices -- Youre doing good!
## ℹ (6/12) Fetching data for INVE-B.st
## !    - not cached
## ✔    - cache saved successfully
## ✔    - got 47 valid rows (2025-03-03 --> 2025-05-09)
## ✔    - got 96% of valid prices -- Well done vincentsmac!
## ℹ (7/12) Fetching data for NDA-SE.ST
## !    - not cached
## ✔    - cache saved successfully
## ✔    - got 47 valid rows (2025-03-03 --> 2025-05-09)
## ✔    - got 96% of valid prices -- You got it vincentsmac!
## ℹ (8/12) Fetching data for ORRON.ST
## !    - not cached
## ✔    - cache saved successfully
## ✔    - got 47 valid rows (2025-03-03 --> 2025-05-09)
## ✔    - got 96% of valid prices -- All OK!
## ℹ (9/12) Fetching data for SAAB-B.ST
## !    - not cached
## ✔    - cache saved successfully
## ✔    - got 47 valid rows (2025-03-03 --> 2025-05-09)
## ✔    - got 96% of valid prices -- Youre doing good!
## ℹ (10/12) Fetching data for SINCH.ST
## !    - not cached
## ✔    - cache saved successfully
## ✔    - got 47 valid rows (2025-03-03 --> 2025-05-09)
## ✔    - got 96% of valid prices -- All OK!
## ℹ (11/12) Fetching data for SSAB-B.ST
## !    - not cached
## ✔    - cache saved successfully
## ✔    - got 47 valid rows (2025-03-03 --> 2025-05-09)
## ✔    - got 96% of valid prices -- You got it vincentsmac!
## ℹ (12/12) Fetching data for VOLCAR-B.ST
## !    - not cached
## ✔    - cache saved successfully
## ✔    - got 47 valid rows (2025-03-03 --> 2025-05-09)
## ✔    - got 96% of valid prices -- Mas bah tche, que coisa linda!
## ℹ Binding price data
## 
## ── Diagnostics ─────────────────────────────────────────────────────────────────
## ✔ Returned dataframe with 564 rows -- Feliz que nem lambari de sanga!
## ℹ Using 78.4 kB at /var/folders/xv/5h23j4p10m94jp1pvq6296x00000gn/T//Rtmpxj2mCH/yf_cache for 13 cache files
## ℹ Out of 12 requested tickers, you got 12 (100%)
# Calculate % change from first available price
df_pct_change <- df_stocks %>%
  group_by(ticker) %>%
  arrange(ref_date) %>%
  mutate(pct_change = 100 * (price_adjusted / first(price_adjusted) - 1))

# Plot % change
ggplot(df_pct_change, aes(x = ref_date, y = pct_change, color = ticker)) +
  geom_line(lindewidth = 1) +
  labs(title = "Stock Performance (% Change from Start)",
       x = "Date", y = "% Change", color = "Stock") +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) +
  theme_minimal()
## Warning in geom_line(lindewidth = 1): Ignoring unknown parameters: `lindewidth`

library(ggthemes)

ggplot(df_pct_change, aes(x = ref_date, y = pct_change, color = ticker)) +
  geom_line(size = 1.2) +
  scale_color_tableau(name = "Stock") +  # Color-blind friendly and distinct
  labs(title = "Stock Performance (% Change from Start)",
       x = "Date", y = "% Change") +
  theme_economist() +  # or try theme_fivethirtyeight()
  scale_y_continuous(labels = scales::percent_format(scale = 1))
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning in check_pal_n(n, max_n): This palette can handle a maximum of 10
## values.You have supplied 12.
## Warning: Removed 94 rows containing missing values or values outside the scale range
## (`geom_line()`).