# 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 ("AAPL", "NVDA", "CAT", "PANW", "MU")

prices <- tq_get(x = symbols,
                 get = "stockprices",
                 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" "CAT"  "MU"   "NVDA" "PANW"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights 
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL        0.2
## 2 CAT         0.2
## 3 MU          0.2
## 4 NVDA        0.2
## 5 PANW        0.2

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 = "quarters",
               col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31  0.0295  
##  2 2013-02-28  0.0332  
##  3 2013-03-28  0.0176  
##  4 2013-04-30 -0.0105  
##  5 2013-05-31  0.0389  
##  6 2013-06-28 -0.0170  
##  7 2013-07-31  0.0484  
##  8 2013-08-30  0.0218  
##  9 2013-09-30  0.0421  
## 10 2013-10-31  0.000630
## # 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   -0.587

6 Plot: Skewness Comparison

Histogram of Expected Returns and Risk

# Calculate sd of portfolio returns
sd_portfolio <- sd(portfolio_returns_tbl$returns)
mean_portfolio <- mean(portfolio_returns_tbl$returns)

portfolio_returns_tbl %>%
  
  # Add a new variable
  mutate(extreme_neg = ifelse(returns < mean_portfolio - 2 * sd_portfolio, "ext_neg",
              "not_ext_neg" )) %>%
  
  ggplot(aes(x = returns, fill = extreme_neg)) +
  geom_histogram(binwidth = 0.003) +
  
  scale_x_continuous(breaks = seq(-0.06, 0.06, 0.02)) +
  
  labs(x = "monthly returns")

Scaterplot of skewness comparisons

# 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)))



# 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? Yes, some individual assets in my portfolio may be more likely to produce extreme positive returns than the portfolio as a whole. My portfolio has a skewness of -0.587, which indicates that its returns are negatively skewed, meaning it has a longer tail toward extreme negative returns. Individual stocks such as NVIDIA (NVDA) and Micron (MU) may have greater potential for extreme positive returns because technology stocks can experience large price increases. However, I would need to compare the individual stocks’ actual skewness values from my scatterplot to determine which ones are more positively skewed than the portfolio. Overall, diversification can help balance the effects of individual stocks’ extreme returns.