# 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.

Choose your stocks.

from 2012-12-31 to present

1 Import stock prices

symbols <- c("SPY", "WMT", "COST", "AMZN")
prices <- tq_get(x = symbols, 
                 get = "stock.prices",
                 from = "2012-12-31")

2 Convert prices to returns (monthly)

asset_returns_tbl <- prices %>%
# I added asset_returns_tbl here (wasn't in video) otherwise the code would not run, and mistake code wouldn't be fixable
# In video
    
    group_by(symbol) %>%

    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly",
                 type       = "log") %>%
    slice(-1) %>%
    #Remove the first row, but since data is group, it will remove the first line of each group
    
    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] "AMZN" "COST" "SPY"  "WMT"
# weights
weights <- c(0.30, 0.30, 0.20, 0.20)
weights
## [1] 0.3 0.3 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN        0.3
## 2 COST        0.3
## 3 SPY         0.2
## 4 WMT         0.2

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: 154 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0427 
##  2 2013-02-28  0.00121
##  3 2013-03-28  0.0363 
##  4 2013-04-30  0.00325
##  5 2013-05-31  0.0201 
##  6 2013-06-28  0.00825
##  7 2013-07-31  0.0616 
##  8 2013-08-30 -0.0526 
##  9 2013-09-30  0.0497 
## 10 2013-10-31  0.0694 
## # ℹ 144 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    0.343

6 Plot: Rolling 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(from = 0, to = 5, by = 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("Downside risk surged again toward the end of 2025"),
             hjust = 0.0005,
             vjust = 3.5)

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.

Over time, the downside risk of my portfolio increased. More precisely, the Kurtosis skyrocketed in 2024, suggesting an increase in likelihood of extreme returns, then experienced a significant drop at the start of 2025, indicating a temporary decrease in risk. However, it increased again shortly after, although it remained lower than the peak Kurtosis levels of 2024. This would indicate that the returns of my portfolio are more concentrated in their tails. This means that my portfolio has bigger chances of experiencing large returns, in this case large losses. This conclusion is supported by the previous plot of the returns distribution’s negative skewness, indicating an increased in downside risk (a higher probability of large negative returns).

So, my portfolio’s level of risk has deteriorated over time, and in recent years, has become more likely to experience downside events.