# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Visualize and compare skewness of your portfolio and its assets.

Choose your stocks.

from 2012-12-31 to 2017-12-31

1 Import stock prices

symbols <- c("TM", "SBUX", "AEO", "BBW")
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 = "quarterly", 
                 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] "AEO"  "BBW"  "SBUX" "TM"
# weights
weights <- c(0.25, 0.25, 0.2, 0.3)
weights
## [1] 0.25 0.25 0.20 0.30
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AEO        0.25
## 2 BBW        0.25
## 3 SBUX       0.2 
## 4 TM         0.3

4 Build a 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: 20 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-03-28  0.108  
##  2 2013-06-28  0.103  
##  3 2013-09-30  0.0238 
##  4 2013-12-31  0.0187 
##  5 2014-03-31 -0.00730
##  6 2014-06-30  0.0916 
##  7 2014-09-30  0.0548 
##  8 2014-12-31  0.136  
##  9 2015-03-31  0.112  
## 10 2015-06-30 -0.0356 
## 11 2015-09-30 -0.00379
## 12 2015-12-31 -0.0806 
## 13 2016-03-31 -0.00559
## 14 2016-06-30 -0.0278 
## 15 2016-09-30  0.00508
## 16 2016-12-30  0.0425 
## 17 2017-03-31 -0.142  
## 18 2017-06-30 -0.00367
## 19 2017-09-29  0.0345 
## 20 2017-12-29  0.108

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   -0.118

6 Plot: Rolling Kurtosis

Rolling 24 Month Kurtosis

# Assign a value for window
window = 12

# Transform data: calculate 12 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(-1,2,0.5)) +
    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("2017-01-01"),
             y = 1, 
             size = 5, 
             color = "red",
             label = str_glue("Downside risk shows an overall 
                              increase from 2016-2018."))

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 previous assignment.

The downside risk of my portfolio has an overall increase from 2016-2018, with a few months where it plateaus. This means that overtime, the potential for these investments to lose value is increasing. The negative kurtosis value is most likely from the very flat BBW distribution. However, AEO and TM also have larger curves which could be contributing to the negative value.

The negative kurtosis value means the assets have thinner tails and there are less extreme values.The negative skew of the portfolio shown in the Scatter Plot of Skewness Comparison graph indicates that there are some extreme outliers on the left side of the distribution. Together, these traits indicated that the portfolio has occasional large losses.The increase in kurtosis means that this could change to frequent large losses.