# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Visualize and compare skewness of your portfolio and its assets.

Choose your stocks.

from 2017-12-31 to 2022-12-31

1 Import stock prices

# Choose stocks
symbols <- c("MSFT", "JPM", "GM", "TMUS", "IRVRF")

prices <- tq_get(x    = symbols, 
                get  = "stock.prices",
                from = "2017-12-31", 
                to   = "2022-12-31")

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] "GM"    "IRVRF" "JPM"   "MSFT"  "TMUS"
# weights
weights <- c(0.25, 0.2, 0.2, 0.1, 0.25)
weights
## [1] 0.25 0.20 0.20 0.10 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GM         0.25
## 2 IRVRF      0.2 
## 3 JPM        0.2 
## 4 MSFT       0.1 
## 5 TMUS       0.25

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: 59 × 2
##    date        returns
##    <date>        <dbl>
##  1 2018-02-28  0.00823
##  2 2018-03-29 -0.0253 
##  3 2018-04-30  0.0341 
##  4 2018-05-31 -0.00672
##  5 2018-06-29 -0.0144 
##  6 2018-07-31 -0.00515
##  7 2018-08-31  0.0271 
##  8 2018-09-28 -0.0164 
##  9 2018-10-31  0.0523 
## 10 2018-11-30  0.0644 
## # … with 49 more rows

5 Compute Skewness

# 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 GM        -0.303
## 2 IRVRF     -0.376
## 3 JPM       -0.339
## 4 MSFT      -0.129
## 5 TMUS       0.363
## 6 Portfolio -0.742

6 Plot: Skewness Comparison

# 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")

# Plot distribution of returns
asset_returns_tbl %>%
    
    ggplot(aes(x = returns)) +
    geom_density(aes(color = asset), show.legend = FALSE, alpha = 1) +
    geom_histogram(aes(fill = asset), show.legend = FALSE, alpha = 0.3, binwidth = 0.01) +
    facet_wrap(~asset, ncol = 1, scales = "free_y") +
    
    # Labeling
    labs(title = "Distribution of Monthly Returns, 2017-2022",
         y = "Frequency",
         x = "Rate of Returns")

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.

All but one of the assets in the portfolio are moderately left-skewed distributions. TMUS is the only asset that is right-skewed, indicating that this asset has a long tail on the right side of its monthly returns distribution. When interpreting the skewness along with the data in the distribution plot, TMUS is more likely to yield large positive gains than the collective portfolio. The other assets are more evenly distributed with slight negative skew, meaning they have small tails on the left side of the center of their distributions. With these assets we can more likely expect frequent small gains and less frequent large losses.