# 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("META", "TSLA", "NKE", "VOO")

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", "return"))

3 Assign a weight to each asset (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "META" "NKE"  "TSLA" "VOO"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2)
weights
## [1] 0.25 0.25 0.20 0.20
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 META       0.25
## 2 NKE        0.25
## 3 TSLA       0.2 
## 4 VOO        0.2

4 Build a portfolio

# ?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col = asset, 
                returns_col = return, 
                weights     = w_tbl,
                rebalance_on = "months",
                col_rename  = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0800
##  2 2013-02-28 -0.0414
##  3 2013-03-28  0.0282
##  4 2013-04-30  0.114 
##  5 2013-05-31  0.0836
##  6 2013-06-28  0.0291
##  7 2013-07-31  0.150 
##  8 2013-08-30  0.0689
##  9 2013-09-30  0.119 
## 10 2013-10-31 -0.0188
## # ℹ 50 more rows

5 Compute Skewness

# Compute Skewness for the Portfolio
portfolio_skew_tbl <- portfolio_returns_tbl %>%
    tq_performance(Ra = returns, 
                   performance_fun = table.Stats) %>%
    select(Skewness)

portfolio_skew_tbl
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1    0.484

6 Plot: Skewness Comparison

# 6 Plot: Skewness Comparison
library(ggplot2)

# Combine individual asset returns and portfolio returns into one table
combined_returns_tbl <- asset_returns_tbl %>%
    select(asset, date, return) %>%
    bind_rows(
        portfolio_returns_tbl %>% mutate(asset = "Portfolio")
    )

# Create a density plot to visualize and compare return distributions and skewness
combined_returns_tbl %>%
    ggplot(aes(x = return, fill = asset, color = asset)) +
    geom_density(alpha = 0.25, linewidth = 1) +
    labs(
        title = "Return Distributions: Individual Assets vs. Portfolio",
        subtitle = "Comparing extreme positive/negative likelihood via skewness",
        x = "Monthly Return",
        y = "Density"
    ) +
    theme_minimal()

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.
Yes, TSLA (and to a lesser extent META) is significantly more likely to return extreme positive returns than the portfolio collectively.