# 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", "F", "WMT", "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" "F"    "MU"   "NVDA" "WMT"
# 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 F           0.2
## 3 MU          0.2
## 4 NVDA        0.2
## 5 WMT         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 0.0105 
##  2 2013-02-28 0.0243 
##  3 2013-03-28 0.0673 
##  4 2013-04-30 0.0186 
##  5 2013-05-31 0.0781 
##  6 2013-06-28 0.00921
##  7 2013-07-31 0.0439 
##  8 2013-08-30 0.00627
##  9 2013-09-30 0.0618 
## 10 2013-10-31 0.0273 
## # 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   -0.437

6 Plot: Skewness Comparison

# 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? Discuss in terms of skewness. You may also refer to the distribution of returns you plotted in Code along 4.
Some individual stocks in my portfolio may have a greater chance of producing extreme positive returns than the portfolio overall. My portfolio has a skewness of -0.587, indicating negatively skewed returns with a greater risk of extreme losses. Stocks such as NVIDIA (NVDA) and Micron (MU) may have more potential for large gains due to their exposure to the technology industry. Apple (AAPL) could also experience significant price increases, while Ford (F) and Walmart (WMT) may have different return patterns. I would need to compare each stock’s actual skewness from my scatterplot to determine which ones are more positively skewed than the portfolio. Overall, diversification helps reduce the impact of extreme returns from individual stocks.