# 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("NVDA", "LLY", "UAA", "HD", "TSLA")

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] "HD"   "LLY"  "NVDA" "TSLA" "UAA"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 HD          0.2
## 2 LLY         0.2
## 3 NVDA        0.2
## 4 TSLA        0.2
## 5 UAA         0.2

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  0.0626 
##  2 2013-02-28 -0.00345
##  3 2013-03-28  0.0396 
##  4 2013-04-30  0.112  
##  5 2013-05-31  0.154  
##  6 2013-06-28 -0.0127 
##  7 2013-07-31  0.0934 
##  8 2013-08-30  0.0505 
##  9 2013-09-30  0.0560 
## 10 2013-10-31 -0.0353 
## # 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    0.392

6 Plot: Skewness Comparison

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.

# 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 HD         0.188
## 2 LLY       -0.220
## 3 NVDA       0.899
## 4 TSLA       0.944
## 5 UAA       -0.884
## 6 Portfolio  0.392
# 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")

asset_returns_tbl %>%
    
    ggplot(aes(x = returns)) +
    geom_density(aes(color = asset), show.legend = FALSE, alpha = 1) +
    geom_histogram(aes(fill = asset), show.legend = FALSE, alpha = 0.3, binwidth = 0.01) + 
    facet_wrap(~asset, ncol = 1, scales = "free_y")  + 
    
    # labeling
    labs(title = "Distribution of Monthly Returns, 2012-2016",
         y = "Frequency",
         x = "Rate of Returns",
         capition = "A typical monthly return is higher for SPY and IJS than for AGG, EEM, and EFA")

Observations made

Reviewing my skewness plot of individual assets in comparison to the portfolio, the following assumptions can be made: My individual stocks with a positive skewness are HD, NVDA, and TSLA. All of these assets display a distribution of returns with a longer right running tail.

My stocks with the highest skewness are NVDA and TSLA. This implies that there is a greater chance of these two stocks having larger positive returns than the rest of the assets in the chart.

LLY and UAA show negative skewness, a long left running tail, with a higher likelihood of extreme negative returns.

Above, I have provided the distribution of monthly returns chart from Apply 4 to help visualize the tails I was referring to