# 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("XOM", "SHEL", "BP", "CVX")
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 BP    2013-01-31  0.0669 
##  2 BP    2013-02-28 -0.0846 
##  3 BP    2013-03-28  0.0471 
##  4 BP    2013-04-30  0.0291 
##  5 BP    2013-05-31 -0.00362
##  6 BP    2013-06-28 -0.0276 
##  7 BP    2013-07-31 -0.00721
##  8 BP    2013-08-30  0.00961
##  9 BP    2013-09-30  0.0175 
## 10 BP    2013-10-31  0.101  
## # ℹ 230 more rows

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

symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "BP"   "CVX"  "SHEL" "XOM"
weights <- c(0.25,0.25,0.25,0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 BP         0.25
## 2 CVX        0.25
## 3 SHEL       0.25
## 4 XOM        0.25

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    tq_portfolio(assets_col   = asset, 
                 returns_col  = returns, 
                 weights      = w_tbl, 
                 rebalance_on = "months")
portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.0477 
##  2 2013-02-28          -0.0292 
##  3 2013-03-28           0.0150 
##  4 2013-04-30           0.0213 
##  5 2013-05-31           0.00575
##  6 2013-06-28          -0.0263 
##  7 2013-07-31           0.0401 
##  8 2013-08-30          -0.0338 
##  9 2013-09-30           0.00754
## 10 2013-10-31           0.0360 
## # ℹ 50 more rows

5 Compute Skewness

asset_skewness_tbl <- asset_returns_tbl %>%
    group_by(asset) %>%
    summarise(skew = skewness(returns)) %>%
    ungroup() %>%
    add_row(tibble(asset = "Portfolio", 
                   skew = skewness(portfolio_returns_tbl$portfolio.returns)))
asset_skewness_tbl
## # A tibble: 5 × 2
##   asset         skew
##   <chr>        <dbl>
## 1 BP         0.229  
## 2 CVX        0.0278 
## 3 SHEL       0.0131 
## 4 XOM       -0.00355
## 5 Portfolio  0.272

6 Plot: Skewness Comparison

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.

Collectively the porfolio is greatly skewed by BP, more so than any one asset is skewed on its own. Being that BP has the record highest monthly return of all these assets it would make sense that it skews the portfolio.