# 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", "MSFT", "TSLA")
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)

weights <- c(0.3, 0.4, 0.3)
weights
## [1] 0.3 0.4 0.3
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN        0.3
## 2 MSFT        0.4
## 3 TSLA        0.3

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  0.0586
##  2 2013-02-28 -0.0153
##  3 2013-03-28  0.0393
##  4 2013-04-30  0.150 
##  5 2013-05-31  0.220 
##  6 2013-06-28  0.0333
##  7 2013-07-31  0.0590
##  8 2013-08-30  0.0701
##  9 2013-09-30  0.0710
## 10 2013-10-31  0.0135
## # ℹ 50 more rows

5 Compute Skewness

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


asset_skewness_tbl
## # A tibble: 4 × 2
##   asset       skew
##   <chr>      <dbl>
## 1 AMZN      0.187 
## 2 MSFT      0.0825
## 3 TSLA      0.944 
## 4 Portfolio 0.535

6 Plot: Skewness Comparison

asset_skewness_tbl %>%
    ggplot(aes(x = asset, y = skew)) +
    geom_point() +
    
    ggrepel::geom_text_repel(aes(label = asset),
                             data = asset_skewness_tbl %>%
                                 filter(asset == "Portfolio")) +
    labs(y = "skewness",
         title = "Skewness Comparison")

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.

The Tesla stock has a higher skewness than my portfolio collectively and therefore will return a greater value.