# 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", "JPM", "NVDA", "WMT")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "JPM"  "NFLX" "NVDA" "WMT"
weights <- c(0.3, 0.2, 0.2, 0.15, 0.15)
weights
## [1] 0.30 0.20 0.20 0.15 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.3 
## 2 JPM        0.2 
## 3 NFLX       0.2 
## 4 NVDA       0.15
## 5 WMT        0.15

4 Build a 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  0.0878 
##  2 2013-02-28  0.0335 
##  3 2013-03-28  0.00738
##  4 2013-04-30  0.0505 
##  5 2013-05-31  0.0409 
##  6 2013-06-28 -0.0636 
##  7 2013-07-31  0.0923 
##  8 2013-08-30  0.0294 
##  9 2013-09-30  0.0251 
## 10 2013-10-31  0.0388 
## # 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   -0.390

6 Plot: Skewness Comparison

asset_skewness_tbl <- asset_returns_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 AAPL      -0.555 
## 2 JPM       -0.330 
## 3 NFLX       0.909 
## 4 NVDA       0.899 
## 5 WMT        0.0723
## 6 Portfolio -0.390
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, both NFLX and NVDA are more likely to experience high positive returns than the portfolio overall because they have positive skewness. Both NVDA and NFLX have a skewness of around 0.9 while the portfolio has negative skewness of around -0.4. This means NFLX and NVDA have distributions with longer right tails, suggesting a greater potential for high positive returns. The portfolio’s negative skewness suggests a greater chance of extreme negative returns. WMT has slightly positive skewness, but it is close to zero. Overall, NFLX and NVDA have the greatest potential for high positive returns compared to the portfolio.