# 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("AAPL", "GOOG", "VOO", "MSFT", "NFLX")
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" "GOOG" "MSFT" "NFLX" "VOO"
# weights
weights <- c(.1, .45, .2, .15, .1)
weights
## [1] 0.10 0.45 0.20 0.15 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.1 
## 2 GOOG       0.45
## 3 MSFT       0.2 
## 4 NFLX       0.15
## 5 VOO        0.1

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  0.112  
##  2 2013-02-28  0.0487 
##  3 2013-03-28  0.00658
##  4 2013-04-30  0.0680 
##  5 2013-05-31  0.0482 
##  6 2013-06-28 -0.0219 
##  7 2013-07-31  0.0279 
##  8 2013-08-30  0.0171 
##  9 2013-09-30  0.0284 
## 10 2013-10-31  0.105  
## # ℹ 50 more rows

5 Compute Skewness

# Transform data: calculate rolling skewness
rolling_skew_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select = returns,
              mutate_fun = rollapply,
              width = 24,
              FUN = skewness,
              col_rename = "Skew") %>%
    
    select(-returns) %>%
    na.omit()

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)))
asset_skewness_tbl
## # A tibble: 6 × 2
##   asset        skew
##   <chr>       <dbl>
## 1 AAPL      -0.555 
## 2 GOOG       0.784 
## 3 MSFT       0.0825
## 4 NFLX       0.909 
## 5 VOO       -0.256 
## 6 Portfolio  0.0333
# 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.

GOOG is more likely to return extreme positive returns than my portfolio. On the scatterplot, you can see that GOOG has the second highest skewness around .75. My portfolio skewness is around .1. NFLX has the highest skew, but it has a much lower distribution of returns than GOOG, as shown in the graph from apply 4, and from the graphic in apply 6 that shows mean returns. In those graphs it appears that GOOG would be more likely to return an extreme positive return.