# 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("NFLX", "AAPL", "TSLA")
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] "AAPL" "NFLX" "TSLA"
# weights
weights <- c(.25, .25, .2)
weights
## [1] 0.25 0.25 0.20
w_tble <- tibble(symbols, weights)
w_tble
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 NFLX       0.25
## 3 TSLA       0.2

4 Build a portfolio

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

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        Returns
##    <date>        <dbl>
##  1 2013-01-31  0.126  
##  2 2013-02-28  0.0111 
##  3 2013-03-28  0.0191 
##  4 2013-04-30  0.104  
##  5 2013-05-31  0.136  
##  6 2013-06-28 -0.0301 
##  7 2013-07-31  0.114  
##  8 2013-08-30  0.103  
##  9 2013-09-30  0.0429 
## 10 2013-10-31 -0.00445
## # … 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  -0.0647

6 Plot: Skewness Comparison

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 NFLX       0.909 
## 3 TSLA       0.944 
## 4 Portfolio -0.0647
# 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.

Tesla is more likely to return extreme positive but the skewness is really high which is not necessarily a good thing when referring to stocks.