# 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("TSLA", "GOOG")
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] "GOOG" "TSLA"
# weights
weights <- c(0.5, 0.5)
weights
## [1] 0.5 0.5
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 2 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GOOG        0.5
## 2 TSLA        0.5

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")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.0841 
##  2 2013-02-28          -0.00782
##  3 2013-03-28           0.0377 
##  4 2013-04-30           0.196  
##  5 2013-05-31           0.324  
##  6 2013-06-28           0.0521 
##  7 2013-07-31           0.116  
##  8 2013-08-30           0.0914 
##  9 2013-09-30           0.0842 
## 10 2013-10-31          -0.0136 
## # … with 50 more rows

5 Compute Skewness

portfolio_skew_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = portfolio.returns, 
                   performance_fun = table.Stats) %>%
    
    select(Skewness)
    
portfolio_skew_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1    0.884

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$portfolio.returns)))

asset_skewness_tbl
## # A tibble: 3 × 2
##   asset      skew
##   <chr>     <dbl>
## 1 GOOG      0.784
## 2 TSLA      0.944
## 3 Portfolio 0.883
# 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.

TSLA seems to bring in more extreme positive returns compared to that of my portfolio. It is important to note that TSLA may report more extreme returns as its skewness is around .94. It is important to note, that goes both negatively and positively with extreme returns. Risk taking investors should see TSLA as a more risky investment because of their extreme rates of return. GOOG on the other hand, sees skewness below .8 around .78 compared to the portfolio skewness of around .87 . A conservative investor should see GOOG as a more preferred stock than TSLA especially when considering more extreme returns to that of the collective portfolio. If one is looking for a higher risk, higher reward stock, TSLA is the stock to choose.