# 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("META", "NFLX", "AMD", "GOOGL", "NVDA")
prices <- tq_get(x    = symbols,
                 get  = "stock.prices", 
                 from = "2012-12-31",
                 to   = "2017-12-31")

2 Convert prices to returns (monthly)

asset_returns_table <- 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_table %>% distinct(asset) %>% pull()
symbols
## [1] "AMD"   "GOOGL" "META"  "NFLX"  "NVDA"
# 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 AMD         0.2
## 2 GOOGL       0.2
## 3 META        0.2
## 4 NFLX        0.2
## 5 NVDA        0.2

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_table %>%
    
    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  0.175  
##  2 2013-02-28  0.0109 
##  3 2013-03-28 -0.00570
##  4 2013-04-30  0.0847 
##  5 2013-05-31  0.0748 
##  6 2013-06-28 -0.00955
##  7 2013-07-31  0.0991 
##  8 2013-08-30  0.0203 
##  9 2013-09-30  0.104  
## 10 2013-10-31  0.00970
## # 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     0.08

6 Plot: Skewness Comparison

# Calculate sd of portfolio

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 = 0.007) +
    
    scale_x_continuous(breaks = seq(-0.06,0.06,0.02)) +

    labs(x = "monthly returns")

Scatterplot of skewness comparison

# Data transformation: calculate skewness
asset_skewness_tbl <- asset_returns_table %>%
    
    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 AMD       0.293 
## 2 GOOGL     0.867 
## 3 META      1.15  
## 4 NFLX      0.909 
## 5 NVDA      0.899 
## 6 Portfolio 0.0800
# Plot skewness

asset_skewness_tbl %>%
    
    ggplot(aes(x = asset, y = skew, color = asset)) +
    geom_point() +

    geom_text(aes(label = asset),
              vjust = 1.5,   # Nudges labels down
              hjust = 0.5,   # Centers labels horizontally
              size  = 4,     # Sets text size
              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.

Answer: The portfolio itself had a very small positive skewness of about 0.075; this would indicate that the skewness of returns is fairly parallel overall. META was the only asset in the portfolio that represented the potential for extreme positive returns given the fact that it had a skewness of around 1.18. AMD had the least potential with a skewness of just below 0.3, informing me that its distribution of returns fell within parallel margins. Lastly, GOOGL, NFLX, and NVDA all had moderately positive skewness of around 0.9.