# 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", "TSLA", "HD", "MSFT", "NKE")

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 %>%

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

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%

    tq_portfolio(assets_col = asset,
                 returns_col = returns,
                 weigh = w_tbl,
                 rebalance_on = "months",
                 col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0444 
##  2 2013-02-28 -0.00879
##  3 2013-03-28  0.0491 
##  4 2013-04-30  0.145  
##  5 2013-05-31  0.175  
##  6 2013-06-28  0.0126 
##  7 2013-07-31  0.0554 
##  8 2013-08-30  0.0621 
##  9 2013-09-30  0.0657 
## 10 2013-10-31 -0.0108 
## # 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    0.925

6 Plot: Rolling kurtosis

window = 12

rolling_kurt_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select     = returns,
              mutate_fun = rollapply,
              width      = window,
              FUN        = kurtosis,
              col_rename = "kurt") %>%
    
    na.omit() %>%
    select(-returns)

rolling_kurt_tbl %>%
    
    ggplot(aes(x = date, y = kurt)) +
    geom_line(color = "cornflowerblue") +
    
    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)) +
    
    labs(x = NULL,
         y = "kurtosis",
         title = paste0("Rolling", window,  "Month Kurtosis")) +
    
    annotate(geom = "text",
             x = as.Date("2016-07-01"), y = 3,
             size = 5, color = "red",
             label = str_glue("Downside risk skyrocketed toward the end of 2017"))

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.