# 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 ("AAPL", "PANW", "NVDA", "CAT", "MU")

prices <- tq_get(x = symbols,
                 get = "stockprices",
                 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] "AAPL" "CAT"  "MU"   "NVDA" "PANW"
# 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 AAPL        0.2
## 2 CAT         0.2
## 3 MU          0.2
## 4 NVDA        0.2
## 5 PANW        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 = "quarters",
               col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31  0.0295  
##  2 2013-02-28  0.0332  
##  3 2013-03-28  0.0176  
##  4 2013-04-30 -0.0105  
##  5 2013-05-31  0.0389  
##  6 2013-06-28 -0.0170  
##  7 2013-07-31  0.0484  
##  8 2013-08-30  0.0218  
##  9 2013-09-30  0.0421  
## 10 2013-10-31  0.000630
## # 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    0.317

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")) +
  
  annotate(geom = "text", 
           x = as.Date("2016-07-01"), 
           y = 3,
           size = 5, color = "red",
           label = "Downside risk skyrocketed
           toward the end of 2018")

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 portfolio’s risk of extreme returns generally increased over time, especially during 2017. The rolling 24-month kurtosis stayed mostly between −1.2 and −0.5 during 2015 and 2016, then rose sharply to about 1.5 by the end of 2017. This suggests greater exposure to unusually large returns near the end of the period.