# 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("MCD", "WEN", "YUM", "DPZ", "SBUX")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2020-12-31")

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] "DPZ"  "MCD"  "SBUX" "WEN"  "YUM"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 DPZ         0.2
## 2 MCD         0.2
## 3 SBUX        0.2
## 4 WEN         0.2
## 5 YUM         0.2

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: 96 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0524 
##  2 2013-02-28  0.0273 
##  3 2013-03-28  0.0496 
##  4 2013-04-30  0.0226 
##  5 2013-05-31  0.0218 
##  6 2013-06-28  0.00976
##  7 2013-07-31  0.0804 
##  8 2013-08-30 -0.00594
##  9 2013-09-30  0.0690 
## 10 2013-10-31  0.00341
## # … with 86 more rows

5 Compute Skewness

portfolio_skew_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    
    select(Skewness)

portfolio_skew_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1   -0.420

6 Plot: Skewness Comparison

# Data transformation: calculate skewness
asset_skewness_tbl <- asset_returns_tbl %>%
    
    group_by(asset) %>%
    summarise(skew = skewness(returns)) %>%
    ungroup() %>%
    
    # Add portfolio skewness
    add_row(tibble(asset = "Portfolio",
                   skew = skewness(portfolio_returns_tbl$returns)))

asset_skewness_tbl
## # A tibble: 6 × 2
##   asset       skew
##   <chr>      <dbl>
## 1 DPZ       -0.106
## 2 MCD       -0.384
## 3 SBUX      -0.440
## 4 WEN        0.207
## 5 YUM       -0.569
## 6 Portfolio -0.420
# Plot skewness
asset_skewness_tbl %>%
    
    ggplot(aes(x = asset, y = skew, color = asset)) +
    geom_point() +
    
    ggrepel::geom_text_repel(aes(label = asset),
                            data = asset_skewness_tbl %>%
                            filter(asset == "Portfolio")) +
    labs(y = "skewness")

Is any asset in your portfolio more likely to return extreme positive returns than your portfolio collectively? Discuss in terms of skewness. You may also refer to the distribution of returns you plotted in Code along 4.

My portfolio as a whole reflects a relatively symmetrical distribution, falling at a skewness of just under -.4. In order to be considered negatively skewed, it would need to fall lower than -.5. The only asset in my portfolio that can be considered skewed is that of YUM, which represents Taco bell company and their other associated food chains. The skewness for YUM falls at almost, but not quite, -.6. This is not a very extreme skew, but is does show an outlying amount of negative returns. Falling at a skewness of positive .2, WEN is the only individual asset of the portfolio to show any propensity for positive returns; however, not enough to be considered technically skewed as it is under .5.