# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Visualize and examine changes in the underlying trend in the downside risk of your portfolio in terms of kurtosis.

1 Import stock prices

symbols <- c("UNH", "LLY", "JNJ", "PFE", "MRK")

prices <- tq_get(x = symbols, 
                 get = "stock.prices", 
                 from = "2010-01-01", 
                 to = "2025-01-01")

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
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "JNJ" "LLY" "MRK" "PFE" "UNH"
# weights
weights <- c(0.3, 0.25, 0.20, 0.13, 0.12)
weights
## [1] 0.30 0.25 0.20 0.13 0.12
w_tbl <-tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 JNJ        0.3 
## 2 LLY        0.25
## 3 MRK        0.2 
## 4 PFE        0.13
## 5 UNH        0.12

4 Build a portfolio

# ?tq_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: 179 × 2
##    date        returns
##    <date>        <dbl>
##  1 2010-02-26 -0.0103 
##  2 2010-03-31  0.0209 
##  3 2010-04-30 -0.0379 
##  4 2010-05-28 -0.0629 
##  5 2010-06-30  0.00805
##  6 2010-07-30  0.0222 
##  7 2010-08-31  0.00400
##  8 2010-09-30  0.0797 
##  9 2010-10-29  0.00154
## 10 2010-11-30 -0.0318 
## # ℹ 169 more rows

5 Compute kurtosis

portfolio_kurt_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    select(Kurtosis)

portfolio_kurt_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1    0.194

6 Plot: Rolling kurtosis

Rolling 24-Month Kurtosis

# Assign a value for window
window = 24

# Transform Data: Calculate 24-Month Rolling Kurtosis
rolling_kurt_tbl <-portfolio_returns_tbl %>%
    
    tq_mutate(select = returns,
              mutate_fun = rollapply,
              width = window,
              FUN = kurtosis,
              col_rename = "kurt") %>%
    
    na.omit() %>%
    select(-returns)

# Plot
rolling_kurt_tbl %>%
    
    ggplot(aes(x = date, y = kurt)) +
    geom_line(color = "cornflowerblue") +
    
    # Formatting
    scale_y_continuous(breaks = seq(-1, 4, 0.5)) +
    scale_x_date(breaks = scales::pretty_breaks(n = 7)) +
    theme(plot.title = element_text(hjust = 0.5)) +
    
    # Labeling
    labs(x = NULL,
         y = "Kurtosis",
         title = paste0("Rolling ", window, " Month Kurtosis")) +
        
    annotate(geom = "text", x = as.Date("2016-07-01"), 
             y = 3,
             size = 5,
             color = "red",
             label = str_glue(""))

Has the downside risk of your portfolio increased or decreased over time? Explain using the plot you created. You may also refer to the skewness of the returns distribution you plotted in the previous assignment.

The kurtosis plot shows a clear decline in tail risk since the end of 2018. The kurtosis spiked towards the end of 2015 and hit its high near the end of 2018. Since then it has been on a downward trend with a small spike in the beginning of 2024. Additionally, since there is no strong negative skew in my returns distribution from apply 7, the downside risk of the portfolio has decreased over time—suggesting the portfolio has become more stable and less prone to extreme losses.