# 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

# Choose stocks

symbols <- c("AAPL", "ROKU", "CL=F")

# Using tq_get() ----
prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2023-10-22")

2 Convert prices to returns (monthly)

asset_returns_tbl <- prices %>%
  
  # Calculate monthly returns
  group_by(symbol) %>%
  tq_transmute(select     = adjusted,
               mutate_fun = periodReturn,
               period     = "monthly",
               type       = "log") %>%
  slice (-1) %>%
  ungroup() %>%
  
  # rename
  set_names(c("asset", "date", "returns"))

# period_returns = c("yearly", "quarterly", "monthly", "weekly")

3 Assign a weight to each asset (change the weigting scheme)

symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()

w <- c(0.15,
       0.35,
       0.50)

w_tbl <- tibble(symbols, w)

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
  
  tq_portfolio(assets_col   = asset,
               returns_col  = returns,
               weights      = w_tbl,
               col_rename   = "returns",
               rebalance_on = "months")

portfolio_returns_tbl
## # A tibble: 130 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.00237
##  2 2013-02-28 -0.0239 
##  3 2013-03-28  0.0196 
##  4 2013-04-30 -0.0138 
##  5 2013-05-31 -0.00230
##  6 2013-06-28 -0.00184
##  7 2013-07-31  0.0492 
##  8 2013-08-30  0.0207 
##  9 2013-09-30 -0.0210 
## 10 2013-10-31 -0.00716
## # ℹ 120 more rows

5 Compute kurtosis

portfolio_returns_tbl %>%
  
  tq_performance(Ra = returns,
                 Rb = NULL,
                 performance_fun = table.Stats) %>%
  select(Kurtosis)
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1     2.72

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(y = "Kurtosis",
       x = NULL,
       title = paste0("Rolling ", window, " Month Kurtosis")) +
  
  annotate(geom = "text",
           x = as.Date("2022-01-01"), y = 5,
           color = "red", size = 5,
           label = str_glue("Downside risk skyrocketed
                            toward the end of 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.

Answer: I would say that the downside risk has decreased a little bit over time, but not a lot. We can see based on the graph that the downside risk has been going up and down since 2016, but it has gone down quite a bit since Covid in 2021. One interesting part of the graph is how the downside risk skyrocketed toward the end of 2017, and then it decreased a lot from 2018-2020. I would also like to add that my skewness last week was positive.