# 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", "DELL")

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] "DELL" "TSLA"
# weights
weights <- c(0.50,0.50)
weights
## [1] 0.5 0.5
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 2 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 DELL        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,
                 col_rename   = "returns",
                 rebalance_on = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0510
##  2 2013-02-28 -0.0371
##  3 2013-03-28  0.0421
##  4 2013-04-30  0.177 
##  5 2013-05-31  0.297 
##  6 2013-06-28  0.0468
##  7 2013-07-31  0.112 
##  8 2013-08-30  0.115 
##  9 2013-09-30  0.0674
## 10 2013-10-31 -0.0949
## # ℹ 50 more rows

5 Compute Skewness

portfolio_skew_tidyquant_building_percent <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    
    select(Skewness) 

portfolio_skew_tidyquant_building_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1    0.783

6 Plot: Skewness Comparison

#Transform data
 mean_kurt_tbl <- asset_returns_tbl %>%
     
     # Calculate mean returns and kurtosis for assets
     group_by(asset) %>%
     summarise(mean = mean(returns),
               kurt = kurtosis(returns)) %>%
         ungroup() %>%
     
     # Add portfolio stats
     add_row(portfolio_returns_tbl %>%
         summarise(mean = mean(returns),
               kurt = kurtosis(returns)) %>%
     mutate(asset = "Portfolio"))
 
 # Plot
 mean_kurt_tbl %>%
     
     ggplot(aes(x = kurt, y = mean)) + 
     geom_point() +
     ggrepel::geom_text_repel(aes(label = asset, color = asset)) + 
     
     # Formatting 
     theme(legend.position = "none") +
     scale_y_continuous(labels = scales::percent_format(accuracy = 0.1)) +
     
     # Labelling
     labs(x = "kurtosis",
          y = "Expected Returns")

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 likely to return extreme positive returns more than my portfolio collectively. It’s mean is above 0.036 while my portfolio is around 0.024 and the standard deviation is just above 0.14 while portfolio is at 0.08