# 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("NKE", "MSFT", "AAPL", "NFLX", "AMZN")
prices <- tq_get(x    = symbols, 
                 from = "2012-12-31", 
                 to = )

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" "AMZN" "MSFT" "NFLX" "NKE"
# 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 AAPL       0.25
## 2 AMZN       0.25
## 3 MSFT       0.2 
## 4 NFLX       0.2 
## 5 NKE        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", 
            col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 142 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.101 
##  2 2013-02-28  0.0237
##  3 2013-03-28  0.0178
##  4 2013-04-30  0.0510
##  5 2013-05-31  0.0387
##  6 2013-06-28 -0.0364
##  7 2013-07-31  0.0652
##  8 2013-08-30  0.0438
##  9 2013-09-30  0.0521
## 10 2013-10-31  0.0861
## # 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     1.57

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 downside risk of my portfolio increased significantly from 2016 to 2022, with an extreme rise at the start of 2022. However in 2024 the downside risk has decreased, which you can tell by the negative values of kurtosis. This means that the portfolio is experiencing less extreme returns and negative outcomes compared to around 2018 and 2022. So if there was a time to invest in the portfolio with the least amount of risk involved I would probably suggest doing so now.