# 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

# Choose stocks

symbols <- c("MSFT", "HD", "TSLA", "AMC", "WAL")

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 %>%

    # Calculate monthly returns
    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] "AMC"  "HD"   "MSFT" "TSLA" "WAL"
# weight
weights <- c(0.25, 
             0.25, 
             0.20, 
             0.20, 
             0.10)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMC        0.25
## 2 HD         0.25
## 3 MSFT       0.2 
## 4 TSLA       0.2 
## 5 WAL        0.1

4 Build a portfolio

portfolio_returns_rebalanced_monthly_tbl <- asset_returns_tbl %>%

    tq_portfolio(assets_col = asset,
                 returns_col = returns,
                 weigh = w_tbl,
                 rebalance_on = "months",
                 col_rename = "returns")

portfolio_returns_rebalanced_monthly_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0611 
##  2 2013-02-28  0.00301
##  3 2013-03-28  0.0326 
##  4 2013-04-30  0.119  
##  5 2013-05-31  0.148  
##  6 2013-06-28  0.0215 
##  7 2013-07-31  0.0448 
##  8 2013-08-30  0.0342 
##  9 2013-09-30  0.0466 
## 10 2013-10-31 -0.00783
## # ℹ 50 more rows

5 Compute Skewness

portfolio_skew_tidyquant_builtin_percent <- portfolio_returns_rebalanced_monthly_tbl %>%

    tq_performance(Ra = returns,
                   Rb = NULL, 
                   performance_fun = table.Stats) %>%

    select(Skewness)

portfolio_skew_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1    0.264

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_returns_rebalanced_monthly_tbl$returns)))

asset_skewness_tbl
## # A tibble: 6 × 2
##   asset        skew
##   <chr>       <dbl>
## 1 AMC       -1.55  
## 2 HD         0.188 
## 3 MSFT       0.0825
## 4 TSLA       0.944 
## 5 WAL       -0.0127
## 6 portfolio  0.264
# 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")
## $y
## [1] "skewness"
## 
## attr(,"class")
## [1] "labels"

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.

my Tesla stock is most likely to have an extreme positive return out of my whole portfolio.