Apply it to your data 7

# 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("SBUX", "AAPL", "VZ", "T")

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"))

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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols

## [1] "AAPL" "SBUX" "T"    "VZ"

# weights
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 AAPL       0.25
## 2 SBUX       0.25
## 3 T          0.25
## 4 VZ         0.25

4 Build a portfolio

# ?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    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 -0.0106 
##  2 2013-02-28  0.0129 
##  3 2013-03-28  0.0292 
##  4 2013-04-30  0.0514 
##  5 2013-05-31 -0.0279 
##  6 2013-06-28 -0.00994
##  7 2013-07-31  0.0557 
##  8 2013-08-30 -0.00316
##  9 2013-09-30  0.0126 
## 10 2013-10-31  0.0799 
## # … with 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   -0.172

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_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.

Within my portfolio the asset that is more likely to return extreme positive returns is VZ. My portfolio skewness is a little higher then -.2, and VZ is the only asset that is higher then the portfolio average at a skewness of about 0.1. Although, when I look back my distribution of monthly returns, VZ has the lowest monthly return, which would make it a risky asset.