# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(ggrepel)

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("CRWD", "AMZN", "SHOP","TTD", "NVDA")

prices <- tq_get(x = symbols, 
                 get  = "stock.prices", 
                 from = "2021-01-01")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMZN" "CRWD" "NVDA" "SHOP" "TTD"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)

w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.25
## 2 CRWD       0.25
## 3 NVDA       0.2 
## 4 SHOP       0.2 
## 5 TTD        0.1

4 Build a 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: 45 × 2
##    date         returns
##    <date>         <dbl>
##  1 2021-02-26  0.0378  
##  2 2021-03-31 -0.0978  
##  3 2021-04-30  0.110   
##  4 2021-05-28  0.00183 
##  5 2021-06-30  0.149   
##  6 2021-07-30 -0.000161
##  7 2021-08-31  0.0648  
##  8 2021-09-30 -0.0992  
##  9 2021-10-29  0.105   
## 10 2021-11-30  0.0333  
## # ℹ 35 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   -0.019

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, " Month Kurtosis"))

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.

My portfolios skewness was slightly above -.5. Meaning there was a tail to the left and the possibility of large negative returns. This means a negative kurtosis for my portfolio is bad. It shows frequent large losses for my portfolio. With a low kurtosis, and a negative Skewness, my portfolio has the chance to have frequent large losses.