# 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("COST", "LOW", "HD", "KR")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31")

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"))

asset_returns_tbl
## # A tibble: 664 × 3
##    asset date        returns
##    <chr> <date>        <dbl>
##  1 COST  2013-01-31  0.0359 
##  2 COST  2013-02-28 -0.00765
##  3 COST  2013-03-28  0.0465 
##  4 COST  2013-04-30  0.0216 
##  5 COST  2013-05-31  0.0138 
##  6 COST  2013-06-28  0.00854
##  7 COST  2013-07-31  0.0601 
##  8 COST  2013-08-30 -0.0458 
##  9 COST  2013-09-30  0.0291 
## 10 COST  2013-10-31  0.0243 
## # ℹ 654 more rows

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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "COST" "HD"   "KR"   "LOW"
# weights
weights <- c(0.35, 0.25, 0.25, 0.15)
weights
## [1] 0.35 0.25 0.25 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 COST       0.35
## 2 HD         0.25
## 3 KR         0.25
## 4 LOW        0.15

4 Build a portfolio

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

portfolio_retuns_tbl
## # A tibble: 166 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0594 
##  2 2013-02-28  0.0176 
##  3 2013-03-28  0.0529 
##  4 2013-04-30  0.0318 
##  5 2013-05-31  0.0319 
##  6 2013-06-28  0.00242
##  7 2013-07-31  0.0716 
##  8 2013-08-30 -0.0434 
##  9 2013-09-30  0.0461 
## 10 2013-10-31  0.0374 
## # ℹ 156 more rows

5 Compute kurtosis

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

portfolio_kurt_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1   -0.508

6 Plot: Rolling kurtosis

# Assign a value for window
window = 24

# Transform data: calculate 24 month rolling kurtosis
rolling_kurt_tbl <- portfolio_retuns_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 = "red") + 
    
    # 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"))

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 decreased over time. My portfolio had a large spike in positive kurtosis in 2018 then has gradually gone down to hover around -0.75 kurtosis for the last two years. The low kurtosis level shows my portfolio has a lesser likelyhood of having extreme returns.