# 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", "ISRG", "KHC", "FIS", "GOOG")

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] "FIS"  "GOOG" "ISRG" "KHC"  "MCD"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 FIS        0.25
## 2 GOOG       0.25
## 3 ISRG       0.2 
## 4 KHC        0.2 
## 5 MCD        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: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0719 
##  2 2013-02-28 -0.00415
##  3 2013-03-28  0.00839
##  4 2013-04-30  0.0271 
##  5 2013-05-31  0.0274 
##  6 2013-06-28 -0.00198
##  7 2013-07-31 -0.0501 
##  8 2013-08-30 -0.00818
##  9 2013-09-30  0.0171 
## 10 2013-10-31  0.0506 
## # ℹ 50 more rows

5 Compute Skewness

portfolio_returns_tbl %>%

    tq_performance(Ra = returns,
                   Rb = NULL,
                   performance_fun = table.Stats) %>%
    select(Skewness)
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1    0.478

6 Plot: Skewness Comparison

# Figure 5.6 Asset and portfolio skewness comparison ----

asset_returns_skew_tbl <- asset_returns_tbl %>%

    # skewness for each asset
    group_by(asset) %>%
    summarise(skew = skewness(returns)) %>%
    ungroup() %>%

    # skewness of portfolio
    add_row(tibble(asset = "Portfolio",
                  skew = skewness(portfolio_returns_tbl$returns)))


asset_returns_skew_tbl %>%

    ggplot(aes(asset, skew, color = asset)) +
    geom_point() +

    # Add label for portfolio
    ggrepel::geom_text_repel(aes(label = asset),
                             data = asset_returns_skew_tbl %>%
                                 filter(asset == "Portfolio"),
                             size = 5,
                             show.legend = FALSE) +
    labs(y = "skewness")

Is any asset in your portfolio more likely to return extreme positive returns than your portfolio collectively?

Among the assets in the portfolio, GOOG shows a skewness of roughly 0.75, indicating a greater likelihood of generating extreme positive returns compared to the overall portfolio skewness of roughly 0.5. While MCD matches the portfolio’s skewness, suggesting an equal likelihood of extreme positive returns, the other assets, particularly FIS and ISRG, show negative skewness, indicating a higher risk of extreme negative returns. Using this information I would say only GOOG is more likely to outperform the portfolio in terms of extreme positive returns.