# 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("WMT", "TGT", "AMZN")

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)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMZN" "TGT"  "WMT"
# weights
weights <- c(0.3, 0.2, 0.1)
weights
## [1] 0.3 0.2 0.1
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN        0.3
## 2 TGT         0.2
## 3 WMT         0.1

4 Build a portfolio

# tq_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  0.0236 
##  2 2013-02-28  0.00920
##  3 2013-03-28  0.0254 
##  4 2013-04-30 -0.00476
##  5 2013-05-31  0.0125 
##  6 2013-06-28  0.00700
##  7 2013-07-31  0.0358 
##  8 2013-08-30 -0.0492 
##  9 2013-09-30  0.0355 
## 10 2013-10-31  0.0519 
## # … with 50 more rows

5 Compute 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  -0.0099

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 AMZN       0.187  
## 2 TGT        0.148  
## 3 WMT        0.0723 
## 4 Portfolio -0.00985
# 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.

With-in the chart it is all subjective. However, based on what we can see we are not as likely to have extreme positive returns from the portfolio compared to the other businesses as the portfolio is under 0% in skewness. Where as Amazon is at around 23% skewness, Target is around 14% skewness and Walmart is around 7.8% skewness compared to the Portfolio that is around -2% skewness.