# 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("NVDA", "TSLA", "HD", "LLY", "UAA")

prices <- tq_get(x    = symbols, 
                 get. = "stock.prices",
                 from = "2012-12-31", 
                 to   = "2017-12-31")

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] "HD"   "LLY"  "NVDA" "TSLA" "UAA"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 HD          0.2
## 2 LLY         0.2
## 3 NVDA        0.2
## 4 TSLA        0.2
## 5 UAA         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: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0626 
##  2 2013-02-28 -0.00345
##  3 2013-03-28  0.0396 
##  4 2013-04-30  0.112  
##  5 2013-05-31  0.154  
##  6 2013-06-28 -0.0127 
##  7 2013-07-31  0.0934 
##  8 2013-08-30  0.0505 
##  9 2013-09-30  0.0560 
## 10 2013-10-31 -0.0353 
## # ℹ 50 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    0.252

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, 1.5, 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("2017-01-01"), 
             y     = 0.5, 
             size  = 5, 
             color = "red",
             label = str_glue("The kurtosis for the portfolio increased 
                              dramatically leading into 2016, 
                              and remained relatively stable after that, 
                              starting to drop early 2017."))

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.

During the period, my kurtosis increased sharply, which also leads to an increase in downside risk. For a time, kurtosis stabilized in 2016 through 2017, eventually declining in 2018, leading to more normal distributions once again. Downside risk decreased as these extreme movements became less frequent.