# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(scales)
library(ggrepel)

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("WMT", "MSFT", "GE")
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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "GE"   "MSFT" "WMT"
#Weights
weights <- c(0.25, 0.25, 0.5)
weights
## [1] 0.25 0.25 0.50
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GE         0.25
## 2 MSFT       0.25
## 3 WMT        0.5

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  0.0342 
##  2 2013-02-28  0.0235 
##  3 2013-03-28  0.0371 
##  4 2013-04-30  0.0463 
##  5 2013-05-31  0.0104 
##  6 2013-06-28 -0.00434
##  7 2013-07-31  0.0147 
##  8 2013-08-30 -0.0291 
##  9 2013-09-30  0.0157 
## 10 2013-10-31  0.0565 
## # … with 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    0.110

6 Plot: Rolling kurtosis

24 month 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 = "Corn Flower Blue") +
    # Formatting
    scale_y_continuous(breaks = seq(-1, 4, .5)) +
    scale_x_date(breaks = scales::pretty_breaks(n = 7)) + 
    theme(plot.title = element_text(hjust = .5)) +
    
    # Labeling
    labs(x = NULL, 
         y = "Kurtosis", 
         title = paste0("Rolling ", window, " Kurtosis")) +
# Annotate
annotate(geom = "text", x = as.Date("2016-07-01"), 
         y = 3, size = 3, color = "red", 
         label = str_glue("Downside risk skyrocketed
                         in mid 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.

Downside risk has increased significantly within the time frame that I am measuring. The Kurtosis findings suggest that the portfolio is less likely to follow a normal distribution curve, and are more likely to have skewed returns. This is supported by the skewness that I found in the previous application problem. This higher level of kurtosis indicates a higher level of varience for the portfolio and therefore greater risk.