# 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("WMT", "NVDA", "AMZN")

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] "AMZN" "NVDA" "WMT"
# weights
weights <- c(0.25, 0.25, 0.5)
weights
## [1] 0.25 0.25 0.50
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.25
## 2 NVDA       0.25
## 3 WMT        0.5

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: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0266 
##  2 2013-02-28  0.0143 
##  3 2013-03-28  0.0365 
##  4 2013-04-30  0.0244 
##  5 2013-05-31  0.0125 
##  6 2013-06-28 -0.00212
##  7 2013-07-31  0.0500 
##  8 2013-08-30 -0.0407 
##  9 2013-09-30  0.0467 
## 10 2013-10-31  0.0505 
## # ℹ 50 more rows

5 Compute kurtosis

# Figure 6.3 Asset and Portfolio Kurtosis Comparison ----

asset_returns_kurtosis_tbl <- asset_returns_tbl %>%

    # kurtosis for each asset
    group_by(asset) %>%
    summarise(kt = kurtosis(returns),
              mean = mean(returns)) %>%
    ungroup() %>%

    # kurtosis of portfolio
    add_row(tibble(asset = "Portfolio",
                   kt = kurtosis(portfolio_returns_tbl$returns),
                   mean = mean(portfolio_returns_tbl$returns)))

asset_returns_kurtosis_tbl %>%

    ggplot(aes(kt, mean)) +
    geom_point() +
    
    # Formatting
    scale_y_continuous(labels = scales::percent_format(accuracy = 0.1)) +
    theme(legend.position = "none") +

    # Add label
    ggrepel::geom_text_repel(aes(label = asset, color = asset), size = 5) +

    labs(y = "Expected Return",
         x = "Kurtosis")

6 Plot: Rolling kurtosis

# 3 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()

# Figure 6.5 Rolling kurtosis ggplot ----

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 skyrocketed at the end of the period
                              with the 24-month kurtosis rising above three."))

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.

From looking at my portfolio, there has been a large increase over the 24 month span. This shows that the portfolio is on a steady increase and has potential to continue on this trend.