# 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("SOXX", "TKR", "VUG", "AMZN", "GOOG")
prices <- tq_get(x = symbols, 
                 from = "2012-12-31",
                 to = "2026-10-28")

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] "AMZN" "GOOG" "SOXX" "TKR"  "VUG"
# 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)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.25
## 2 GOOG       0.25
## 3 SOXX       0.2 
## 4 TKR        0.2 
## 5 VUG        0.1

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")

portfolio_returns_tbl
## # A tibble: 166 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.0725 
##  2 2013-02-28           0.0249 
##  3 2013-03-28           0.0163 
##  4 2013-04-30          -0.0124 
##  5 2013-05-31           0.0574 
##  6 2013-06-28           0.00642
##  7 2013-07-31           0.0398 
##  8 2013-08-30          -0.0465 
##  9 2013-09-30           0.0688 
## 10 2013-10-31           0.0626 
## # ℹ 156 more rows

5 Compute kurtosis

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

portfolio_kurt_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1     1.21

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 = portfolio.returns,
              mutate_fun = rollapply,
              width = window,
              FUN = kurtosis,
              col_rename = "kurt") %>%
    
    na.omit() %>% 
    select(-portfolio.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("2018-12-31"), y =3, 
             size = 5, color = "red",
             label = str_glue("Decreasing trend, ignoring extreme outliers"))

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.

Downside risk has massively fluctuated but ultimately decreased over time. I can attribute the large thin spikes to the covid pandemic, the start of the Russian-Ukrainian war, and the most recent middle-eastern ongoing conflict. If you look past those global events, the graph is quite stable with a general decreasing trend.