# 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("SBUX", "AAPL", "VZ", "T")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2022-12-31")

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" "SBUX" "T"    "VZ"
# 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 AAPL       0.25
## 2 SBUX       0.25
## 3 T          0.25
## 4 VZ         0.25

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: 120 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.0106 
##  2 2013-02-28  0.0129 
##  3 2013-03-28  0.0292 
##  4 2013-04-30  0.0514 
##  5 2013-05-31 -0.0279 
##  6 2013-06-28 -0.00994
##  7 2013-07-31  0.0557 
##  8 2013-08-30 -0.00316
##  9 2013-09-30  0.0126 
## 10 2013-10-31  0.0799 
## # … with 110 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    0.004

6 Plot: Rolling kurtosis

# Assign a value to winder
window <- 24
port_rolling_kurtosis_tbl <- portfolio_returns_tbl %>%
    tq_mutate(select = returns,
              mutate_fun = rollapply,
              width      = window,
              FUN        = kurtosis,
              col_rename = "rolling_kurtosis") %>%
    select(date, rolling_kurtosis) %>%
    na.omit()
port_rolling_kurtosis_tbl %>%
    ggplot(aes(date, rolling_kurtosis)) +
    geom_line(color = "cornflowerblue") +
    scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
    scale_x_date(breaks = scales::breaks_pretty(n = 7)) +
    labs(title = paste0("Rolling ", window, "-Month Kurtosis"),
         x = NULL,
         y = "kurtosis") +
    theme(plot.title = element_text(hjust = 0.5)) +
    annotate(geom = "text",
             x = as.Date("2016-12-01"), y = 3,
             color = "red", size = 5,
             label = str_glue("The risk level slightly lowerd at the end of the period
                              with the 24-month kurtosis going down to -1"))

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 slightly decreased over time. When you refer to my rolling 24-month kurtosis graph you can see that the kurtosis started at around 0.25 and is currently around -1. When I look at my skewness graph, I can see my portfolio weakness is around -0.2. This means my portfolio has a slight downside risk. What I can gather from the two graphs is that my portfolio doesn’t have significant downside risk.