# 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("RTX", "GD", "LMT", "BA")
stock_prices <- tq_get(x    = symbols, 
                 get  = "stock.prices", 
                 from = "2012-12-31", 
                 to   = "2017-12-31")

2 Convert prices to returns (monthly)

ar_table <- stock_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 <- ar_table %>% distinct(asset) %>% pull()
symbols
## [1] "BA"  "GD"  "LMT" "RTX"
# weights
weights <- c(0.35, 0.30, 0.20, 0.15)
weights
## [1] 0.35 0.30 0.20 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 BA         0.35
## 2 GD         0.3 
## 3 LMT        0.2 
## 4 RTX        0.15

4 Build a portfolio

portfolio_returns_table <- ar_table %>%
    
    tq_portfolio(assets_col   = asset, 
                 returns_col  = returns, 
                 weights      = w_tbl, 
                 rebalance_on = "months", 
                 col_rename   = ("returns"))
portfolio_returns_table
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31 -0.0224
##  2 2013-02-28  0.0349
##  3 2013-03-28  0.0727
##  4 2013-04-30  0.0405
##  5 2013-05-31  0.0642
##  6 2013-06-28  0.0184
##  7 2013-07-31  0.0764
##  8 2013-08-30 -0.0114
##  9 2013-09-30  0.0773
## 10 2013-10-31  0.0423
## # … with 50 more rows

5 Calculate Kurtosis

portfolio_kurt_tidyquant_builtin_percent <- portfolio_returns_table %>% 
    
    tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    
    select(Kurtosis) 

portfolio_kurt_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1    0.320

6 Plot: Rolling kurtosis

# Assign a value for window
window = 24

# Transform data: calculate 24 month rolling kurtosis
rolling_kurt_table <- portfolio_returns_table %>% 
    
    tq_mutate(select     = returns, 
              mutate_fun = rollapply, 
              width      = window, 
              FUN        = kurtosis, 
              col_rename = "kurt") %>%
    
    na.omit() %>%
    select(-returns)

# Plot
rolling_kurt_table %>% 
    
    ggplot(aes(x = date, y = kurt)) + 
    geom_line(color = "slateblue") + 
    
    # 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 = 1.5, 
             size = 5, 
             color = "red", 
             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.

The downside risk of my portfolio has increases over this time. The first thing we need to look at to see this is the skewness plot from last week. From that plot we are able to see that the portfolio has negative skewness. This means that a majority of returns are going to be on the left of a graph or negative. And as the kurtosis increases, the amount of returns on the left side of the graph will increase which will make the tail thicker on that side.