# 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("AMZN", "AAPL", "TSLA", "NFLX", "GOOGL")

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns

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

symbols <- asset_returns_tbl %>% distinct(asset) %>% pull
symbols
## [1] "AAPL"  "AMZN"  "GOOGL" "NFLX"  "TSLA"
weight <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 AAPL      0.25
## 2 AMZN      0.25
## 3 GOOGL     0.2 
## 4 NFLX      0.2 
## 5 TSLA      0.1

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: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.115 
##  2 2013-02-28  0.0226
##  3 2013-03-28  0.0107
##  4 2013-04-30  0.0573
##  5 2013-05-31  0.0998
##  6 2013-06-28 -0.0261
##  7 2013-07-31  0.107 
##  8 2013-08-30  0.0462
##  9 2013-09-30  0.0585
## 10 2013-10-31  0.0830
## # ℹ 50 more rows

5 Calculate 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   -0.412

6 Plot: Skewness Comparison

# Calculate sd of portfolio

sd_portfolio <- sd(portfolio_returns_tbl$returns)
mean_portfolio <- mean(portfolio_returns_tbl$returns)

portfolio_returns_tbl %>%
    
    # Add a new variable
    mutate(extreme_neg = ifelse(returns < mean_portfolio - 2 * sd_portfolio, "ext_neg", "not_ext_neg")) %>%
    
    ggplot(aes(x = returns, fill = extreme_neg)) +
    geom_histogram(binwidth = 0.007) +
    
    scale_x_continuous(breaks = seq(-0.06,0.06,0.02)) +

    labs(x = "monthly returns")

# Data transformation: calculate skewness
asset_returns_skew_tbl <- asset_returns_tbl %>%

    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? Discuss in terms of skewness. You may also refer to the distribution of returns you plotted in Code along 4.

Yes. Some assets like GOOGL, NFLX, and TSLA have positive skewness, meaning they are more likely to experience large positive returns. The portfolio, with negative skewness, is less likely to see extreme gains because diversification smooths out individual asset swings and reduces the impact of outliers.