# 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("NKE", "VOO", "META", "QQQM")

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", "return"))

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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols <- c("NKE", "VOO", "META", "QQQM")

# 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 NKE        0.25
## 2 VOO        0.25
## 3 META       0.25
## 4 QQQM       0.25

4 Build a portfolio

# ?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col = asset, 
                returns_col = return, 
                weights     = w_tbl,
                rebalace_on = "months",
                col_rename  = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0621 
##  2 2013-02-28 -0.0287 
##  3 2013-03-28  0.0143 
##  4 2013-04-30  0.0444 
##  5 2013-05-31 -0.0325 
##  6 2013-06-28  0.00947
##  7 2013-07-31  0.0930 
##  8 2013-08-30  0.0235 
##  9 2013-09-30  0.103  
## 10 2013-10-31  0.0209 
## # 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  -0.0731

6 Plot: Rolling kurtosis

portfolio_returns_tbl %>%
  tq_mutate(
    select = returns,
    mutate_fun = rollapply,
    width = 24,
    by.column = FALSE,
    FUN = kurtosis,
    col_rename = "rolling_kurtosis"
  ) %>%
  ggplot(aes(x = date, y = rolling_kurtosis)) +
  geom_line() +
  labs(
    title = "Rolling 24 Month Kurtosis",
    x = "",
    y = "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.
As shown on the “Rolling 24 Month Kurtosis” chart, the kurtosis values started relatively high around 2014–2015, peaked sharply in 2015, and then steadily trended downward through 2016 and 2017 (hovering mostly in the negative values). This downward trend indicates that the frequency of extreme outlier returns decreased, meaning the portfolio’s tail risk/downside risk diminished during that period.