# 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("AAPL", "GOOG", "VOO", "MSFT", "NFLX")
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"))

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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "GOOG" "MSFT" "NFLX" "VOO"
# weights
weights <- c(.1, .45, .2, .15, .1)
weights
## [1] 0.10 0.45 0.20 0.15 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.1 
## 2 GOOG       0.45
## 3 MSFT       0.2 
## 4 NFLX       0.15
## 5 VOO        0.1

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: 166 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.112  
##  2 2013-02-28  0.0487 
##  3 2013-03-28  0.00658
##  4 2013-04-30  0.0680 
##  5 2013-05-31  0.0482 
##  6 2013-06-28 -0.0219 
##  7 2013-07-31  0.0279 
##  8 2013-08-30  0.0171 
##  9 2013-09-30  0.0284 
## 10 2013-10-31  0.105  
## # ℹ 156 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     1.24

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, .5)) +
    scale_x_date(breaks = scales::pretty_breaks(n = 7)) + 
    theme(plot.title = element_text(hjust = .5)) +
    
    # labelling
    labs(x = NULL,
         y = "Kurtosis",
         title = "Rolling 24 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 increased a bit over time but it may not be as extreme as the kurtosis plot shows. My portfolio’s skew is slightly above zero, meaning when there are extreme returns they are more likely positive, but it does still increase the risk as the kurtosis grows. In the first few years plotted (2014- 2018) the kurtosis is negative meaning the returns are not extreme. After that it seems to vary highly. It will spike for a year or two and then drop to negative again for part of the next year and this pattern has been repeating. I would not describe the risk as steadily rising but instead I would say that it has a pattern of spiking every few years, which still does not necessarily mean extreme downsides as my skew is above zero.