# 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("UPS", "FDX", "MSFT")
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      date   returns 
##   "asset"    "date" "returns"

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

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

# weights
weights <- c(0.5, 0.3, 0.2)
weights
## [1] 0.5 0.3 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 FDX         0.5
## 2 MSFT        0.3
## 3 UPS         0.2

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    tq_portfolio(assets_col = symbol, 
                 returns_col = monthly.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  0.0732
##  2 2013-02-28  0.0353
##  3 2013-03-28 -0.0185
##  4 2013-04-30  0.0218
##  5 2013-05-31  0.0318
##  6 2013-06-28  0.0105
##  7 2013-07-31  0.0126
##  8 2013-08-30  0.0214
##  9 2013-09-30  0.0432
## 10 2013-10-31  0.102 
## # … with 50 more rows

5 Compute Skewness

portfolio_skew_tiddyquant_builtin_percent <- portfolio_returns_tbl %>%

tq_performance(Ra = returns,
              performance_fun = table.Stats) %>%
    
    select(Skewness) 

portfolio_skew_tiddyquant_builtin_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1   -0.179

6 Plot: Skewness Comparison

# Data Transfermation: Calculate Skewness
asset_skewness_tbl <- asset_returns_tbl %>%
    group_by(symbol) %>%
    summarize(skew = skewness(monthly.returns)) %>%
    ungroup() %>%
    
    ## Add portfolio skewness
add_row(tibble(symbol = "portfolio",
                skew = skewness(portfolio_returns_tbl$returns)))
    
asset_skewness_tbl
## # A tibble: 4 × 2
##   symbol       skew
##   <chr>       <dbl>
## 1 FDX       -0.0655
## 2 MSFT       0.0825
## 3 UPS       -0.628 
## 4 portfolio -0.179
# plot sknewness 
asset_skewness_tbl%>%
    
ggplot(aes(x = symbol, y = skew, color = symbol)) + 
    geom_point() +
    ggrepel::geom_text_repel(aes(label = symbol),
                             data = asset_skewness_tbl %>%
                                 filter(symbol == "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.

I have one stock that has a positive skewness. That stock is Microsoft. This means that there is a higher chance that the returns will fall the the positive side of the spectrum.