# 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", "QQQ", "SPY", "TSLA","CGC")

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] "CGC"  "QQQ"  "SPY"  "TSLA" "XOM"
# weights
weights <- c(0.25,0.25,0.2,0.2,0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 CGC        0.25
## 2 QQQ        0.25
## 3 SPY        0.2 
## 4 TSLA       0.2 
## 5 XOM        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  0.0409 
##  2 2013-02-28 -0.0113 
##  3 2013-03-28  0.0324 
##  4 2013-04-30  0.0796 
##  5 2013-05-31  0.135  
##  6 2013-06-28  0.00985
##  7 2013-07-31  0.0738 
##  8 2013-08-30  0.0323 
##  9 2013-09-30  0.0437 
## 10 2013-10-31 -0.0127 
## # ℹ 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    0.162

6 Plot: Skewness Comparison

#Calculate sd of portfolio returns
sd_portfolio <- sd(portfolio_returns_tbl$returns)
mean_portfolio <- mean(portfolio_returns_tbl$returns)

portfolio_returns_tbl %>%
    
    # Add a new variable
    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) + 
    scale_x_continuous(breaks = seq(-0.06,0.06,0.02))

labs(x = "monthly returns")
## $x
## [1] "monthly returns"
## 
## attr(,"class")
## [1] "labels"
# 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")

Rolling Skewness

# Rolling Skew Table
rolling_skew_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select = returns,
              mutate_fun = rollapply, 
              width      = 24,
              FUN        = skewness,
              col_rename = "Skew") %>%
    select(-returns) %>%
    na.omit()

# Plot
rolling_skew_tbl %>%
    
    ggplot(aes(x = date, y= Skew)) +
    geom_line(color = "orange") +
    
    geom_hline(yintercept = 0, linetype = "dotted", size = 2) +
    # Formatting
    scale_y_continuous(limits = c(-1,1), breaks = seq(-1,1,0.2)) + theme(plot.title = element_text(hjust =0.05)) +

#Labeling
labs(y ="skewness",
     x = NULL,
     title = "Rolling 24-Month Skewness")

    annotate(geom = "text",
             x = as.Date("2016-07-01"),
             y = 0.08,
             color = "red", size = 5,
             label = str_glue("The 24 month rolling skewness is positive for about half of the lifetime, 
                                                                   even though the overall skewness is negative"))
## mapping: x = ~x, y = ~y 
## geom_text: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity

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.

In my portfolio there are two stocks have a much higher skewness than my portfolio returns. CGC and TSLA have a positive skewness of almost 1. Which means those two stocks are more likely to have extreme positive returns. Versus my portfolio has a skewness of almost 0.2 which means my portfolio is almost 5 times less likely to have a extreme positive return than the stocks of TSLA and CGC.