# 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 ("DLR", "NVDA", "GOOG", "MSFT", "AMZN")

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] "AMZN" "DLR"  "GOOG" "MSFT" "NVDA"
# 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 AMZN        0.2
## 2 DLR         0.2
## 3 GOOG        0.2
## 4 MSFT        0.2
## 5 NVDA        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  0.0301 
##  2 2013-02-28  0.0201 
##  3 2013-03-28  0.0101 
##  4 2013-04-30  0.0516 
##  5 2013-05-31  0.0164 
##  6 2013-06-28  0.00167
##  7 2013-07-31 -0.0124 
##  8 2013-08-30 -0.00816
##  9 2013-09-30  0.0336 
## 10 2013-10-31  0.0489 
## # 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    0.845

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 2015")

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. Looking at the plot, the downside risk of my portfolio increased around the end of 2015, when the kurtosis briefly increased. However, it never went above 3, so the tails were not thicker than a normal distribution. After the spike, the kurtosis decreased again. Since my portfolio also had moderately positive skewness (+0.70) and no negative skewness, I would say that the portfolio had a temporary increase in downside risk, but overall the downside risk remained relatively low.