Fiancial Analytics Project
Vincent Fosser
May - 2025
options(repos = c(CRAN = "https://cloud.r-project.org"))Stock Trends Since Tariffs
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 rowsStock Prices Graphed
stocks %>%
ggplot(aes(x = date, y = adjusted, color = symbol)) +
geom_line()
Improved Graph
# 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 stuff!## 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 -- Good stuff!## 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 -- Well done vincentsmac!## 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 -- Good job 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 -- Good stuff!## 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 -- Good stuff!## 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 -- Looking 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 -- Good job vincentsmac!## 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 -- Well done 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 -- Good stuff!## ℹ Binding price data## ## ── Diagnostics ─────────────────────────────────────────────────────────────────## ✔ Returned dataframe with 564 rows -- Nice!## ℹ Using 78.4 kB at /var/folders/xv/5h23j4p10m94jp1pvq6296x00000gn/T//Rtmpm7nz1R/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`
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()`).
Filtering Stocks in positive change and negative change
# Show final positive change stocks
stocks_positive <- df_pct_change %>%
group_by(ticker) %>%
filter(ref_date == max(ref_date)) %>%
filter(pct_change > 0)
kable(stocks_positive)| ticker | ref_date | price_open | price_high | price_low | price_close | volume | price_adjusted | ret_adjusted_prices | ret_closing_prices | cumret_adjusted_prices | pct_change |
|---|---|---|---|---|---|---|---|---|---|---|---|
| AXFO.ST | 2025-05-09 | 269.20 | 270.60 | 267.10 | 269.90 | 176075 | 269.90 | 0.0055887 | 0.0055887 | 1.206617 | 20.661679 |
| SAAB-B.ST | 2025-05-09 | 464.65 | 465.90 | 447.75 | 449.80 | 2762459 | 449.80 | -0.0330001 | -0.0330001 | 1.264794 | 26.479437 |
| SINCH.ST | 2025-05-09 | 26.23 | 26.77 | 24.07 | 24.16 | 13445247 | 24.16 | -0.0555121 | -0.0555121 | 1.013423 | 1.342281 |
stocks_negative <- df_pct_change %>%
group_by(ticker) %>%
filter(ref_date == max(ref_date)) %>%
filter(pct_change < 0)
kable(stocks_negative)| ticker | ref_date | price_open | price_high | price_low | price_close | volume | price_adjusted | ret_adjusted_prices | ret_closing_prices | cumret_adjusted_prices | pct_change |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ATCO-A.ST | 2025-05-09 | 152.500 | 153.60 | 150.950 | 151.500 | 4320357 | 151.500 | -0.0039448 | -0.0039448 | 0.8442671 | -15.573293 |
| AZN.ST | 2025-05-09 | 1311.000 | 1331.50 | 1306.500 | 1323.000 | 451695 | 1323.000 | 0.0022727 | 0.0022727 | 0.8232732 | -17.672682 |
| CAST.ST | 2025-05-09 | 111.000 | 114.40 | 110.250 | 113.850 | 2283814 | 113.850 | 0.0242915 | 0.0242915 | 0.9718484 | -2.815157 |
| ERIC-B.ST | 2025-05-09 | 81.240 | 81.46 | 80.120 | 80.460 | 4388823 | 80.460 | -0.0066667 | -0.0066667 | 0.9174985 | -8.250146 |
| INVE-B.st | 2025-05-09 | 283.700 | 285.85 | 282.550 | 284.150 | 2198796 | 284.150 | 0.0026464 | 0.0026464 | 0.8864178 | -11.358216 |
| NDA-SE.ST | 2025-05-09 | 136.000 | 136.35 | 135.350 | 135.550 | 2672041 | 135.550 | -0.0011053 | -0.0011053 | 0.9593089 | -4.069107 |
| ORRON.ST | 2025-05-09 | 4.178 | 4.35 | 4.178 | 4.254 | 373123 | 4.254 | 0.0201439 | 0.0201439 | 0.7771283 | -22.287172 |
| SSAB-B.ST | 2025-05-09 | 60.180 | 60.30 | 57.880 | 58.820 | 3151423 | 58.820 | -0.0199933 | -0.0199933 | 0.9157264 | -8.427364 |
| VOLCAR-B.ST | 2025-05-09 | 18.500 | 18.65 | 17.820 | 18.060 | 4351761 | 18.060 | -0.0131147 | -0.0131147 | 0.8113207 | -18.867928 |
Arranging Stocks from Best to Worst Performance
stocks_ranked <- df_pct_change %>%
group_by(ticker) %>%
filter(ref_date == max(ref_date)) %>%
ungroup() %>%
arrange(desc(pct_change))
kable(stocks_ranked)| ticker | ref_date | price_open | price_high | price_low | price_close | volume | price_adjusted | ret_adjusted_prices | ret_closing_prices | cumret_adjusted_prices | pct_change |
|---|---|---|---|---|---|---|---|---|---|---|---|
| SAAB-B.ST | 2025-05-09 | 464.650 | 465.90 | 447.750 | 449.800 | 2762459 | 449.800 | -0.0330001 | -0.0330001 | 1.2647944 | 26.479437 |
| AXFO.ST | 2025-05-09 | 269.200 | 270.60 | 267.100 | 269.900 | 176075 | 269.900 | 0.0055887 | 0.0055887 | 1.2066168 | 20.661679 |
| SINCH.ST | 2025-05-09 | 26.230 | 26.77 | 24.070 | 24.160 | 13445247 | 24.160 | -0.0555121 | -0.0555121 | 1.0134228 | 1.342281 |
| CAST.ST | 2025-05-09 | 111.000 | 114.40 | 110.250 | 113.850 | 2283814 | 113.850 | 0.0242915 | 0.0242915 | 0.9718484 | -2.815157 |
| NDA-SE.ST | 2025-05-09 | 136.000 | 136.35 | 135.350 | 135.550 | 2672041 | 135.550 | -0.0011053 | -0.0011053 | 0.9593089 | -4.069107 |
| ERIC-B.ST | 2025-05-09 | 81.240 | 81.46 | 80.120 | 80.460 | 4388823 | 80.460 | -0.0066667 | -0.0066667 | 0.9174985 | -8.250146 |
| SSAB-B.ST | 2025-05-09 | 60.180 | 60.30 | 57.880 | 58.820 | 3151423 | 58.820 | -0.0199933 | -0.0199933 | 0.9157264 | -8.427364 |
| INVE-B.st | 2025-05-09 | 283.700 | 285.85 | 282.550 | 284.150 | 2198796 | 284.150 | 0.0026464 | 0.0026464 | 0.8864178 | -11.358216 |
| ATCO-A.ST | 2025-05-09 | 152.500 | 153.60 | 150.950 | 151.500 | 4320357 | 151.500 | -0.0039448 | -0.0039448 | 0.8442671 | -15.573293 |
| AZN.ST | 2025-05-09 | 1311.000 | 1331.50 | 1306.500 | 1323.000 | 451695 | 1323.000 | 0.0022727 | 0.0022727 | 0.8232732 | -17.672682 |
| VOLCAR-B.ST | 2025-05-09 | 18.500 | 18.65 | 17.820 | 18.060 | 4351761 | 18.060 | -0.0131147 | -0.0131147 | 0.8113207 | -18.867928 |
| ORRON.ST | 2025-05-09 | 4.178 | 4.35 | 4.178 | 4.254 | 373123 | 4.254 | 0.0201439 | 0.0201439 | 0.7771283 | -22.287172 |
#Bar Chart
ggplot(stocks_ranked, aes(x = reorder(ticker, pct_change), y = pct_change, fill = pct_change > 0)) +
geom_col(show.legend = FALSE) +
coord_flip() +
labs(title = "Final % Change in Stock Prices (2025-03-01 to 2025-05-10)",
x = "Stock", y = "% Change") +
scale_y_continuous(labels = scales::percent_format(scale = 1)) +
scale_fill_manual(values = c("TRUE" = "forestgreen", "FALSE" = "red")) +
theme_minimal()
Selected Columns - Ticker & Final % Change
stocks_ranked %>%
select(Stock = ticker, `Final % Change` = pct_change) %>%
kable()| Stock | Final % Change |
|---|---|
| SAAB-B.ST | 26.479437 |
| AXFO.ST | 20.661679 |
| SINCH.ST | 1.342281 |
| CAST.ST | -2.815157 |
| NDA-SE.ST | -4.069107 |
| ERIC-B.ST | -8.250146 |
| SSAB-B.ST | -8.427364 |
| INVE-B.st | -11.358216 |
| ATCO-A.ST | -15.573293 |
| AZN.ST | -17.672682 |
| VOLCAR-B.ST | -18.867928 |
| ORRON.ST | -22.287172 |
Added Column - Positive Or Negative
df_pct_change %>%
group_by(ticker) %>%
filter(ref_date == max(ref_date)) %>%
ungroup() %>%
mutate(growth_category = ifelse(pct_change > 0, "Positive", "Negative")) %>%
select(ticker, pct_change, growth_category) %>%
kable()| ticker | pct_change | growth_category |
|---|---|---|
| ATCO-A.ST | -15.573293 | Negative |
| AXFO.ST | 20.661679 | Positive |
| AZN.ST | -17.672682 | Negative |
| CAST.ST | -2.815157 | Negative |
| ERIC-B.ST | -8.250146 | Negative |
| INVE-B.st | -11.358216 | Negative |
| NDA-SE.ST | -4.069107 | Negative |
| ORRON.ST | -22.287172 | Negative |
| SAAB-B.ST | 26.479437 | Positive |
| SINCH.ST | 1.342281 | Positive |
| SSAB-B.ST | -8.427364 | Negative |
| VOLCAR-B.ST | -18.867928 | Negative |
Summarize Groups by Average % change in each growth category
avg_growth <- df_pct_change %>%
group_by(ticker) %>%
filter(ref_date == max(ref_date)) %>%
ungroup() %>%
mutate(growth_category = ifelse(pct_change > 0, "Positive", "Negative")) %>%
group_by(growth_category) %>%
summarise(avg_change = mean(pct_change))
ggplot(avg_growth, aes(x = growth_category, y = avg_change, fill = growth_category)) +
geom_col() +
geom_text(aes(label = paste0(round(avg_change, 1), "%")), # add % label
vjust = -0.5, size = 4) + # position above the bar
labs(title = "Average % Change by Growth Category",
x = "Growth Category",
y = "Average % Change") +
scale_y_continuous(labels = percent_format(scale = 1)) +
theme_minimal()