# 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("TSLA", "NVDA", "GOOGL", "ORCL", "JNJ")
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
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "GOOGL" "JNJ"   "NVDA"  "ORCL"  "TSLA"
# weights
weight <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 GOOGL     0.25
## 2 JNJ       0.25
## 3 NVDA      0.2 
## 4 ORCL      0.2 
## 5 TSLA      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  0.0527 
##  2 2013-02-28  0.0169 
##  3 2013-03-28  0.0146 
##  4 2013-04-30  0.0728 
##  5 2013-05-31  0.0888 
##  6 2013-06-28 -0.00817
##  7 2013-07-31  0.0626 
##  8 2013-08-30 -0.00442
##  9 2013-09-30  0.0415 
## 10 2013-10-31  0.0361 
## # 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    0.275

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 GOOGL      0.867 
## 2 JNJ       -0.0651
## 3 NVDA       0.899 
## 4 ORCL      -0.0945
## 5 TSLA       0.944 
## 6 portfolio  0.275
# Plot skewness
asset_skewness_tbl %>%
    
    ggplot(aes(x = asset, y = skew, color = asset)) +
    geom_point() + geom_text(aes(label = asset), vjust = 1.5, hjust = 0.5, size = 4, data = asset_skewness_tbl %>% filter(asset == "portfolio")) + 
    labs(y = "skewness") %>% na.omit()

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.
GOOGL, TSLA and NVDA have extreme positive returns in terms of skewness in comparison to my full portfolio. My portfolio has just over 0.25, while those 3 assets are upwards of 0.75, indicating they’re positively skewed.