Apply it to your data 8

# 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("RGR", "TSLA", "AMZN")
prices <- tq_get(x    = symbols, 
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2024-10-23")

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" "RGR"  "TSLA"

# weights
weights <- c(0.25, 0.2, 0.1)
weights

## [1] 0.25 0.20 0.10

w_tbl <- tibble(symbols, weights)
w_tbl

## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.25
## 2 RGR        0.2 
## 3 TSLA       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: 142 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0467 
##  2 2013-02-28  0.00601
##  3 2013-03-28 -0.00280
##  4 2013-04-30  0.0253 
##  5 2013-05-31  0.0721 
##  6 2013-06-28  0.00802
##  7 2013-07-31  0.0542 
##  8 2013-08-30  0.0138 
##  9 2013-09-30  0.0759 
## 10 2013-10-31  0.0278 
## # ℹ 132 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   0.0598

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 = "lightsalmon") +
    
    # Formatting
    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)) +
     
    # Labeling
    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 2024"))

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.

Apply it to your data 8

How has your portfolio’s downside risk changed over time?

Ryan Berg

2024-10-23