# 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("AAPL", "MSFT", "AMZN", "JPM", "XOM")
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 weighting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "AMZN" "JPM"  "MSFT" "XOM"
# weights
weights <- c(0.3, 0.25, 0.20, 0.15, 0.10)
weights
## [1] 0.30 0.25 0.20 0.15 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.3 
## 2 AMZN       0.25
## 3 JPM        0.2 
## 4 MSFT       0.15
## 5 XOM        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 -0.00962
##  2 2013-02-28  0.00226
##  3 2013-03-28  0.00182
##  4 2013-04-30  0.0162 
##  5 2013-05-31  0.0543 
##  6 2013-06-28 -0.0384 
##  7 2013-07-31  0.0637 
##  8 2013-08-30 -0.0112 
##  9 2013-09-30  0.0229 
## 10 2013-10-31  0.0799 
## # ℹ 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  -0.0188

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: 6 × 2
##   asset         skew
##   <chr>        <dbl>
## 1 AAPL      -0.555  
## 2 AMZN       0.187  
## 3 JPM       -0.330  
## 4 MSFT       0.0825 
## 5 XOM       -0.00356
## 6 Portfolio -0.0188
# 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.

Amazon has the highest positive skewness of 0.187, compared to my portfolio’s skewness of -0.0188. This suggests that Amazon’s return distribution has a more pronounced right tail, indicating greater potential for extreme positive returns relative to its overall distribution. Microsoft also has a positive skewness of 0.0825, which is higher than the portfolio’s. Apple and JPMorgan have negative skewness, indicating more pronounced left tails and greater concern about extreme negative returns. ExxonMobil’s skewness is close to zero, suggesting a relatively symmetric distribution. My portfolio has a very slight negative skewness, meaning its left tail is slightly more pronounced, although the distribution is nearly symmetric. A risk-loving investor may find Amazon’s upside potential attractive, while a risk-averse investor should consider the frequency and magnitude of negative returns before making an investment decision.