Apply it to your data 6
How does your portfolio expect perform regarding expected returns and risk?
Vincent Fosser
2025-06-03
# Load packages
# Core
library(tidyverse)
library(tidyquant)Goal
Visualize expected returns and risk to make it easier to compare the performance of multiple assets and portfolios.
Choose your stocks.
1 Import stock prices
symbols <- c("Asker.st", "Atco-B.st", "Axfo.st", "Bahn-b.st", "BRK-B", "Cers", "LLY", "Embrac-b.st", "Indu-c.st", "Inve-b.st", "Inwi.st", "Novo-b.co", "NVDA", "Yubico.st")
prices <- tq_get(x = symbols,
get = "stock.prices",
from = "2020-04-01",
to = "2025-06-01")
prices## # A tibble: 16,672 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Asker.st 2025-03-27 83 87.2 80.2 83.7 16441271 83.7
## 2 Asker.st 2025-03-28 83 84.0 81.7 82 1262083 82
## 3 Asker.st 2025-03-31 81.3 81.9 80.1 80.5 626988 80.5
## 4 Asker.st 2025-04-01 80.8 82.2 80.6 81.9 356628 81.9
## 5 Asker.st 2025-04-02 81.9 82.1 80.9 82.1 576561 82.1
## 6 Asker.st 2025-04-03 81 81.8 80.1 80.7 235131 80.7
## 7 Asker.st 2025-04-04 80.5 81.2 77.3 78.6 780928 78.6
## 8 Asker.st 2025-04-07 74.8 80.1 71.4 77.8 377461 77.8
## 9 Asker.st 2025-04-08 79.2 79.9 75 77 371563 77
## 10 Asker.st 2025-04-09 76.1 78.7 72.8 74.2 1171607 74.2
## # ℹ 16,662 more rows2 Convert prices to returns (monthly)
asset_returns_tbl <- prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
type = "log") %>%
slice(-1) %>%
ungroup() %>%
set_names(c("asset", "date", "returns"))3 Assign a weight to each asset (change the weigting scheme)
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols## [1] "Asker.st" "Atco-B.st" "Axfo.st" "BRK-B" "Bahn-b.st"
## [6] "Cers" "Embrac-b.st" "Indu-c.st" "Inve-b.st" "Inwi.st"
## [11] "LLY" "NVDA" "Novo-b.co" "Yubico.st"weights <- c(0.0314, 0.0133, 0.0136, 0.0589, 0.0112, 0.0068, 0.0201, 0.1858, 0.2298, 0.0584, 0.0892, 0.2504, 0.0168, 0.0143)
weights## [1] 0.0314 0.0133 0.0136 0.0589 0.0112 0.0068 0.0201 0.1858 0.2298 0.0584
## [11] 0.0892 0.2504 0.0168 0.0143w_tbl <- tibble(symbols, weights)
w_tbl## # A tibble: 14 × 2
## symbols weights
## <chr> <dbl>
## 1 Asker.st 0.0314
## 2 Atco-B.st 0.0133
## 3 Axfo.st 0.0136
## 4 BRK-B 0.0589
## 5 Bahn-b.st 0.0112
## 6 Cers 0.0068
## 7 Embrac-b.st 0.0201
## 8 Indu-c.st 0.186
## 9 Inve-b.st 0.230
## 10 Inwi.st 0.0584
## 11 LLY 0.0892
## 12 NVDA 0.250
## 13 Novo-b.co 0.0168
## 14 Yubico.st 0.01434 Build a portfolio
portfolio_returns_tbl <- asset_returns_tbl %>%
tq_portfolio(assets_col = asset,
returns_col = returns,
weights = w_tbl,
rebalance_on = "months")
portfolio_returns_tbl## # A tibble: 68 × 2
## date portfolio.returns
## <date> <dbl>
## 1 2020-05-29 0.0285
## 2 2020-06-30 0.0121
## 3 2020-07-31 0.0243
## 4 2020-08-31 0.0373
## 5 2020-09-30 0.0430
## 6 2020-10-30 -0.0622
## 7 2020-11-30 0.0746
## 8 2020-12-30 0.0297
## 9 2020-12-31 0.0128
## 10 2021-01-29 0.0187
## # ℹ 58 more rows5 Compute Standard Deviation
portfolio_sd_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
tq_performance(Ra = portfolio.returns,
performance_fun = table.Stats) %>%
select(Stdev) %>%
mutate(tq_sd = round(Stdev, 4))
portfolio_sd_tidyquant_builtin_percent## # A tibble: 1 × 2
## Stdev tq_sd
## <dbl> <dbl>
## 1 0.0431 0.0431# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)
portfolio_mean_tidyquant_builtin_percent## [1] 0.013092676 Plot: Expected Returns versus Risk
# Expected Returns vs Risk
sd_mean_tbl <- asset_returns_tbl %>%
group_by(asset) %>%
tq_performance(Ra = returns,
performance_fun = table.Stats) %>%
select(Mean = ArithmeticMean, Stdev) %>%
ungroup() %>%
# Add portfolio
add_row(tibble(asset = "Portfolio",
Mean = portfolio_mean_tidyquant_builtin_percent,
Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))
sd_mean_tbl## # A tibble: 15 × 3
## asset Mean Stdev
## <chr> <dbl> <dbl>
## 1 Asker.st 0.125 0.0918
## 2 Atco-B.st 0.0113 0.0711
## 3 Axfo.st 0.0077 0.0748
## 4 BRK-B 0.0162 0.0571
## 5 Bahn-b.st 0.0166 0.0733
## 6 Cers -0.0259 0.15
## 7 Embrac-b.st -0.0162 0.170
## 8 Indu-c.st 0.0106 0.0589
## 9 Inve-b.st 0.0154 0.0564
## 10 Inwi.st 0.0249 0.0862
## 11 LLY 0.0267 0.0912
## 12 NVDA 0.0479 0.143
## 13 Novo-b.co 0.0134 0.0779
## 14 Yubico.st 0.007 0.110
## 15 Portfolio 0.0131 0.0431sd_mean_tbl %>%
ggplot(aes(x = Stdev, y = Mean, color = asset)) +
geom_point() +
ggrepel::geom_text_repel(aes(label = asset))
rolling_sd_tbl <- portfolio_returns_tbl %>%
tq_mutate(select = portfolio.returns,
mutate_fun = rollapply,
width = 24,
FUN = sd,
col_rename = "rolling_sd") %>%
na.omit() %>%
select(date, rolling_sd)
rolling_sd_tbl## # A tibble: 45 × 2
## date rolling_sd
## <date> <dbl>
## 1 2021-12-31 0.0340
## 2 2022-01-31 0.0405
## 3 2022-02-28 0.0418
## 4 2022-03-31 0.0429
## 5 2022-04-29 0.0435
## 6 2022-05-31 0.0432
## 7 2022-06-30 0.0424
## 8 2022-07-29 0.0435
## 9 2022-08-31 0.0482
## 10 2022-09-30 0.0493
## # ℹ 35 more rowsrolling_sd_tbl %>%
ggplot(aes(x = date, y = rolling_sd)) +
geom_line(color = "royalblue") +
# Formatting
scale_y_continuous(labels = scales::percent_format()) +
# Labeling
labs(x = NULL,
y = NULL,
title = "24-Months Rolling Volatility") +
theme(plot.title = element_text(hjust = .5))
How should you expect your portfolio to perform relative to its assets in the portfolio? Would you invest all your money in any of the individual stocks instead of the portfolio? Discuss both in terms of expected return and risk.
The portfolio would perform somewhat worse than some of the individual assets in the portfolio, assets like Nividia or Asker have performed a lot better. However, the individual assets that perform strong also come with a higher standard deviation the portfolio average standard deviation is 0.0431 with Nvidia being 0.14 and Asker 0.09. The portfolio also holds stocks like Embracer and Cers that have a very high standard deviation of 0.17 and 0.15 which indicates high volatility and risk. The weight of these assets are low however which means their volatility doesn’t affect the entire portfolio a lot. The portfolio offers a moderate return with lowered volatility, looking at the individual assets they are all more volatile than the overall portfolio. This means that the portfolios diversification is working. If I was to invest all money into a single asset it would be into Eli & Lilly, Inwido, Berkshire Hathaway, Bahnhof or Investor since they offer a slightly better return with a slightly higher volatility. In conlusion the portfolio is expected to perform good with consistent returns and lowered risk.