# 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("GOOG", "GME", "NVDA", "V")

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 
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "GME"  "GOOG" "NVDA" "V"
# weights
weights <- c(0.25, 0.25, 0.25, 0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GME        0.25
## 2 GOOG       0.25
## 3 NVDA       0.25
## 4 V          0.25

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")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.00716
##  2 2013-02-28           0.0451 
##  3 2013-03-28           0.0484 
##  4 2013-04-30           0.0804 
##  5 2013-05-31           0.0311 
##  6 2013-06-28           0.0607 
##  7 2013-07-31           0.0398 
##  8 2013-08-30          -0.00126
##  9 2013-09-30           0.0418 
## 10 2013-10-31           0.0666 
## # ℹ 50 more rows

5 Compute kurtosis

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

portfolio_kurt_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1  -0.0603

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     = portfolio.returns, 
              mutate_fun = rollapply,
              width      = window,
              FUN        = kurtosis,
              col_rename = "kurt") %>%
    
    na.omit() %>%
    select(-portfolio.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     = 2.5,,
             size  = 5, 
             color = "red",
             label = str_glue("Downside risk was relatively stable, there 
                              was a slight peak in the beginning of 2016"))

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, for the most part, remained below 0.5. There was a slight peak from around 2015-08 to the middle of 2016. During this period, the downside risk was the highest it has been but not high compared to what it could be. It stayed below 1 which is not bad.