# 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", "TSLA", "NFLX", "MTN", "DIS")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "DIS"  "MTN"  "NFLX" "TSLA"
weight  <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 AAPL      0.25
## 2 DIS       0.25
## 3 MTN       0.2 
## 4 NFLX      0.2 
## 5 TSLA      0.1

4 Build a 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  0.102 
##  2 2013-02-28  0.0242
##  3 2013-03-28  0.0451
##  4 2013-04-30  0.0806
##  5 2013-05-31  0.0871
##  6 2013-06-28 -0.0431
##  7 2013-07-31  0.108 
##  8 2013-08-30  0.0608
##  9 2013-09-30  0.0437
## 10 2013-10-31  0.0315
## # ℹ 50 more rows

5 Compute Skewness

portfolio_skew_tidyquant_builtin_percent <- portfolio_returns_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   -0.521

6 Plot: Skewness Comparison

Histogram of Expected Returns vs Risk

sd_portfolio <- sd(portfolio_returns_tbl$returns)
mean_portfolio <- mean(portfolio_returns_tbl$returns)

portfolio_returns_tbl %>% 
    
    mutate(extreme_neg = ifelse(returns < mean_portfolio - 2 * sd_portfolio,
                                "ext_neg",
                                "not_ext_neg"))%>%
    
    ggplot(aes(x = returns, fill = extreme_neg)) + 
    geom_histogram(binwidth = 0.003, alpha = 0.7) +
    
    scale_x_continuous(breaks = seq(-0.06, 0.06, 0.02)) + 
    
    labs(x = "monthly returns")

Scatterplot of skewness comparison

asset_returns_skew_tbl <- asset_returns_tbl %>%

    group_by(asset) %>%
    summarise(skew = skewness(returns)) %>%
    ungroup() %>%

    add_row(tibble(asset = "Portfolio",
                  skew = skewness(portfolio_returns_tbl$returns)))
asset_returns_skew_tbl %>%

    ggplot(aes(asset, skew, color = asset)) +
    geom_point() +

    # Add label for portfolio
    ggrepel::geom_text_repel(aes(label = asset),
                             data = asset_returns_skew_tbl %>%
                                 filter(asset == "Portfolio"),
                             size = 5,
                             show.legend = FALSE) +
    labs(y = "skewness")

24 Months Rolling Volatility

window <- 24

port_rolling_sd_tbl <- portfolio_returns_tbl %>%

    tq_mutate(select = returns,
              mutate_fun = rollapply,
              width      = window,
              FUN        = skewness,
              col_rename = "rolling_skew") %>%
    select(date, rolling_skew) %>%
    na.omit()

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.

Tesla is more likly to return extremly positive returns vs the entire portfolio collectively.