# 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("X", "ZEUS", "CMC", "TSLA", "GOOG")
prices <- tq_get(x  = symbols,
    get = "stock.prices", 
     from = "2012-12-31",
      to  = "2017-12-31")

2 Convert prices to returns (monthly)

asset_return_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 <- asset_return_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "CMC"  "GOOG" "TSLA" "X"    "ZEUS"
weights <- c(0.20, 0.25, 0.2, 0.2, 0.15)
weights
## [1] 0.20 0.25 0.20 0.20 0.15
w_tbl <- tibble(symbols, weights)
w_tbl 
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 CMC        0.2 
## 2 GOOG       0.25
## 3 TSLA       0.2 
## 4 X          0.2 
## 5 ZEUS       0.15

4 Build a portfolio

portfolio_returns_tbl <- asset_return_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  0.0404 
##  2 2013-02-28 -0.0202 
##  3 2013-03-28  0.0174 
##  4 2013-04-30  0.0207 
##  5 2013-05-31  0.178  
##  6 2013-06-28  0.00592
##  7 2013-07-31  0.0752 
##  8 2013-08-30  0.0226 
##  9 2013-09-30  0.0995 
## 10 2013-10-31  0.0544 
## # ℹ 50 more rows

5 Compute Skewness

portfolio_skew_tidyquant_builitin_percent <- portfolio_returns_tbl %>%

 tq_performance(Ra = returns,  
                performance_fun = table.Stats) %>%
 
   select(Skewness) 

portfolio_skew_tidyquant_builitin_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1    0.442

6 Plot: Skewness Comparison

asset_skewness_tbl <- asset_return_tbl %>% 
    
    group_by(asset) %>%
    summarise(skew = skewness(returns)) %>%
    ungroup() %>%
        
        add_row(tibble(asset = "Portfolio", 
                       skew = skewness(portfolio_returns_tbl$returns)))
asset_skewness_tbl
## # A tibble: 6 × 2
##   asset      skew
##   <chr>     <dbl>
## 1 CMC       1.18 
## 2 GOOG      0.784
## 3 TSLA      0.944
## 4 X         0.364
## 5 ZEUS      0.153
## 6 Portfolio 0.442
    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.

Yes, the stock CMC has a much higher skewness than my portfolio and even the other assets. It has a larger range of returns where it has a extreme positive return and returns that deviate negetively in a more extreme way as well. This makes the asset look to be more volatile and risky where you don’t know exactly what you are going to get in terms of a return.