# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Visualize and compare skewness of your portfolio and its assets.

Choose your stocks.

“MSFT”, “AAPL”, “F”, “JPM”, “SBUX” from 2012-12-31 to 2017-12-31

1 Import stock prices

symbols <- c("MSFT", "AAPL", "F", "JPM", "SBUX")

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] "AAPL" "F"    "JPM"  "MSFT" "SBUX"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)

4 Build a portfolio

# ?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col = asset,
                 returns_col = returns,
                 weigts = w_tbl,
                 rebalance_on = "months", 
                 col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31 -0.000198
##  2 2013-02-28 -0.00229 
##  3 2013-03-28  0.0162  
##  4 2013-04-30  0.0584  
##  5 2013-05-31  0.0744  
##  6 2013-06-28 -0.0293  
##  7 2013-07-31  0.0580  
##  8 2013-08-30 -0.00248 
##  9 2013-09-30  0.0252  
## 10 2013-10-31  0.0460  
## # 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   0.0498

6 Plot: Skewness Comparison

# Data transformation : calculating 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.

Based on the skewness values shown in the graph, Microsoft (MSFT) is the asset in the portfolio most likely to return extreme positive returns compared to the portfolio collectively. MSFT displays the highest positive skewness among all assets, significantly higher than the portfolio’s slightly positive skewness. This indicates that Microsoft’s returns have a more pronounced tendency towards extreme positive outcomes than the overall portfolio, making it the standout asset for potential outsized gains.