# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(PerformanceAnalytics)
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("NKE", "TSLA", "MSFT", "AAPL", "JPM")

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] "AAPL" "JPM"  "MSFT" "NKE"  "TSLA"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 JPM        0.25
## 3 MSFT       0.2 
## 4 NKE        0.2 
## 5 TSLA       0.1

4 Build a 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  0.00469
##  2 2013-02-28  0.00240
##  3 2013-03-28  0.0234 
##  4 2013-04-30  0.0892 
##  5 2013-05-31  0.0983 
##  6 2013-06-28 -0.0261 
##  7 2013-07-31  0.0521 
##  8 2013-08-30  0.0299 
##  9 2013-09-30  0.0420 
## 10 2013-10-31  0.0260 
## # ℹ 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   -0.227

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(x = NULL,
         y = "Kurtosis",
         title = paste0("Rolling ", window, "-Month Kurtosis"))

7 Skewness

skewness(portfolio_returns_tbl$returns)
## [1] -0.2735515

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 portfolio has a skewness of around -0.27. This means the distribution is approximately symmetrical. There is not much risk for large losses or large gains. The kurtosis over time starts around zero, dips down to around -1.7 and then back up close to zero. These are all low kurtosis values meaning the distribution has skinny tails with little downside risk. The portfolio has very little downside risk, but also very little chance of large gains.