# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Visualize and compare skewness of your portfolio and its assets.

Choose your stocks. (AAPL, ROKU, CL=F)

from 2012-12-31 to 2017-12-31

1 Import stock prices

# Choose stocks

symbols <- c("AAPL", "ROKU", "CL=F")

# Using tq_get() ----
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 %>%
  
  # Calculate monthly returns
  group_by(symbol) %>%
  tq_transmute(select     = adjusted,
               mutate_fun = periodReturn,
               period     = "monthly",
               type       = "log") %>%
  slice (-1) %>%
  ungroup() %>%
  
  # rename
  set_names(c("asset", "date", "returns"))

# period_returns = c("yearly", "quarterly", "monthly", "weekly")

3 Assign a weight to each asset

symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()

w <- c(0.45,
       0.35,
       0.20)

w_tbl <- tibble(symbols, w)

4 Build a portfolio

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

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.0490 
##  2 2013-02-28 -0.0316 
##  3 2013-03-28  0.0204 
##  4 2013-04-30 -0.0137 
##  5 2013-05-31  0.00435
##  6 2013-06-28 -0.0396 
##  7 2013-07-31  0.0889 
##  8 2013-08-30  0.0448 
##  9 2013-09-30 -0.0275 
## 10 2013-10-31  0.0204 
## # ℹ 50 more rows

5 Calculate Skewness

portfolio_returns_tbl %>%
  
  tq_performance(Ra = returns,
                 Rb = NULL,
                 performance_fun = table.Stats) %>%
  select(Skewness)
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1    0.416

6 Plot: Skewness Comparison

# 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: 4 × 2
##   asset       skew
##   <chr>      <dbl>
## 1 AAPL      -0.555
## 2 CL=F      -0.244
## 3 ROKU       0.202
## 4 Portfolio  0.415
# 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")

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.

Based on the skewness graph, none of my assets are more likely to return extreme positive returns than my portfolio collectively. as a matter of fact, Apple is more likely to return very negative returns compared to my portfolio, which I found interesting. My portfolio as one has the highest skewness, and Roku is the only one of my three stocks that has a positive skewness.