# 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.

“MSFT”, “AAPL”, “F”, “JPM”, “SBUX”

from 2012-12-31 to present

1 Import stock prices

symbols <- c("MSFT", "AAPL", "F", "JPM", "SBUX")

prices <- tq_get(x    = symbols,
                 from = "2012-12-31")

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] "AAPL" "F"    "JPM"  "MSFT" "SBUX"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)

4 Build a portfolio

# ?tq_portfolio

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

portfolio_returns_tbl
## # A tibble: 142 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31 -0.000198
##  2 2013-02-28 -0.00229 
##  3 2013-03-28  0.0162  
##  4 2013-04-30  0.0584  
##  5 2013-05-31  0.0744  
##  6 2013-06-28 -0.0293  
##  7 2013-07-31  0.0580  
##  8 2013-08-30 -0.00248 
##  9 2013-09-30  0.0252  
## 10 2013-10-31  0.0460  
## # ℹ 132 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     1.33

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(-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, "Monthly Kurtosis")) +
    
    annotate(geom = "text", x = as.Date("2020-07-01"), y = 5,
             size = 5, color = "red",
             label = "Downside risk has fluctuated over time")

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 that downside risk has decreased over time, with major spikes occurring during the 2020 COVID crash (~4.5) and in 2022 (~3.0). While current levels have returned to 2016 values, the pattern of more frequent spikes suggests markets have become more prone to extreme events.