# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

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 rows

2 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 0.0143
w_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    0.0143

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col   = asset, 
                 returns_col  = returns, 
                 weights      = w_tbl, 
                 rebalance_on = "months",
                 col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 68 × 2
##    date       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 rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x    =  "SPY", 
                             from  = "2020-04-01",
                             to    = "2025-06-01") %>%  
   
    # Convert Prices to Returns  
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 type      = "log", 
                 col_rename = "returns") %>% 
    
    slice(-1) 

market_returns_tbl
## # A tibble: 61 × 2
##    date       returns
##    <date>       <dbl>
##  1 2020-05-29  0.0465
##  2 2020-06-30  0.0176
##  3 2020-07-31  0.0572
##  4 2020-08-31  0.0675
##  5 2020-09-30 -0.0382
##  6 2020-10-30 -0.0252
##  7 2020-11-30  0.103 
##  8 2020-12-31  0.0364
##  9 2021-01-29 -0.0102
## 10 2021-02-26  0.0274
## # ℹ 51 more rows

5.2 Join returns

portfolio_market_returns_tbl <- left_join(market_returns_tbl, portfolio_returns_tbl, by = "date") %>% 
    
    set_names("date", "market_returns", "portfolio_returns")

portfolio_market_returns_tbl
## # A tibble: 61 × 3
##    date       market_returns portfolio_returns
##    <date>              <dbl>             <dbl>
##  1 2020-05-29         0.0465            0.0285
##  2 2020-06-30         0.0176            0.0121
##  3 2020-07-31         0.0572            0.0243
##  4 2020-08-31         0.0675            0.0373
##  5 2020-09-30        -0.0382            0.0430
##  6 2020-10-30        -0.0252           -0.0622
##  7 2020-11-30         0.103             0.0746
##  8 2020-12-31         0.0364            0.0128
##  9 2021-01-29        -0.0102            0.0187
## 10 2021-02-26         0.0274            0.0310
## # ℹ 51 more rows

5.3 CAPM Beta

portfolio_market_returns_tbl %>% 
    
    tq_performance(Ra = portfolio_returns, 
                   Rb = market_returns, 
                   performance_fun = CAPM.beta )
## # A tibble: 1 × 1
##   CAPM.beta.1
##         <dbl>
## 1       0.672

6 Plot: Scatter with regression line

portfolio_market_returns_tbl %>%
    
    ggplot(aes(x = market_returns, 
               y = portfolio_returns)) +
    geom_point(color = "cornflowerblue") +
    geom_smooth(method = "lm", se = FALSE, 
                size = 1.5, color = 
tidyquant::palette_light()[3]) + 
    
    labs(y = "Portfolio Returns", 
         x = "Market Returns")

How sensitive is your portfolio to the market? Discuss in terms of the beta coefficient. Does the plot confirm the beta coefficient you calculated?

My portfolio is less volatily compared to the market, looking at the beta coefficient I calculated it shows a beta of 0.672 which indicates less risk. I think it is in line with what the graph indicates.