# 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

#choose stocks 
symbols <- c("COST", "TSLA", "NFLX", "GOOG")

prices <- tq_get(x     = symbols, 
                 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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols 
## [1] "COST" "GOOG" "NFLX" "TSLA"
# weights 
weights <- c(0.25, 0.25, 0.25, 0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl 
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 COST       0.25
## 2 GOOG       0.25
## 3 NFLX       0.25
## 4 TSLA       0.25

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 0.196  
##  2 2013-02-28 0.0265 
##  3 2013-03-28 0.0321 
##  4 2013-04-30 0.136  
##  5 2013-05-31 0.177  
##  6 2013-06-28 0.0108 
##  7 2013-07-31 0.110  
##  8 2013-08-30 0.0716 
##  9 2013-09-30 0.0707 
## 10 2013-10-31 0.00978
## # 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   0.0592

6 Plot: Skewness Comparison

# Data transformation: calcualte skewdness 
asset_skewness_tbl <- asset_returns_tbl %>% 
    
    group_by(asset) %>% 
    summarise(skew = skewness(returns)) %>%  
    ungroup() %>% 
    
    #add portfolio skewdness 
    add_row(tibble(asset = "Portfolio", 
                   skew = skewness(portfolio_returns_tbl$returns)))

asset_skewness_tbl 
## # A tibble: 5 × 2
##   asset        skew
##   <chr>       <dbl>
## 1 COST      -0.244 
## 2 GOOG       0.784 
## 3 NFLX       0.909 
## 4 TSLA       0.944 
## 5 Portfolio  0.0592
# 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")
## $y
## [1] "Skewness"
## 
## attr(,"class")
## [1] "labels"

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 assets GOOG, NFLX, and TSLA all posses a higher skewness than my portfolio, with TSLA having the highest level of skewness, at about 0.95. And when looking at the histogram out of those three assets, NFLX and TSLA both posses a right-skewed distribution. However it is difficult to say which would more likely gain higher positive returns, due to how both curves are somewhat flat.