# 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("COST", "LOW", "HD", "KR")

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"))

asset_returns_tbl
## # A tibble: 240 × 3
##    asset date        returns
##    <chr> <date>        <dbl>
##  1 COST  2013-01-31  0.0359 
##  2 COST  2013-02-28 -0.00765
##  3 COST  2013-03-28  0.0465 
##  4 COST  2013-04-30  0.0216 
##  5 COST  2013-05-31  0.0138 
##  6 COST  2013-06-28  0.00854
##  7 COST  2013-07-31  0.0601 
##  8 COST  2013-08-30 -0.0458 
##  9 COST  2013-09-30  0.0291 
## 10 COST  2013-10-31  0.0243 
## # ℹ 230 more rows

3 Assign a weight to each asset (change the weighting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "COST" "HD"   "KR"   "LOW"
# weights
weights <- c(0.35, 0.25, 0.25, 0.15)
weights
## [1] 0.35 0.25 0.25 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 COST       0.35
## 2 HD         0.25
## 3 KR         0.25
## 4 LOW        0.15

4 Build a portfolio

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

portfolio_retuns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0594 
##  2 2013-02-28  0.0176 
##  3 2013-03-28  0.0529 
##  4 2013-04-30  0.0318 
##  5 2013-05-31  0.0319 
##  6 2013-06-28  0.00242
##  7 2013-07-31  0.0716 
##  8 2013-08-30 -0.0434 
##  9 2013-09-30  0.0461 
## 10 2013-10-31  0.0374 
## # ℹ 50 more rows

5 Compute Skewness

portfolio_skew_tidyquant_builtin_percent <- portfolio_retuns_tbl %>% 
    
    tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    
    select(Skewness) 

portfolio_skew_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1   -0.380

6 Plot: Skewness Comparison

# 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_retuns_tbl$returns)))

asset_skewness_tbl
## # A tibble: 5 × 2
##   asset        skew
##   <chr>       <dbl>
## 1 COST      -0.244 
## 2 HD         0.188 
## 3 KR        -0.432 
## 4 LOW       -0.0308
## 5 Portfolio -0.380
# 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.

The only asset with an extreme positive is LOW however it was an outlier with mostly average returns besides the one return. HD is the only asset with a positive skewness.