# 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", "MSFT", "GOOG", "AMZN", "META")

prices <- tq_get(x = symbols,
                 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] "AMZN" "GOOG" "META" "MSFT" "NVDA"
#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 AMZN       0.25
## 2 GOOG       0.25
## 3 META       0.2 
## 4 MSFT       0.2 
## 5 NVDA       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,
               rebalence_on = "months",
               col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0665 
##  2 2013-02-28 -0.00663
##  3 2013-03-28 -0.00526
##  4 2013-04-30  0.0493 
##  5 2013-05-31  0.0226 
##  6 2013-06-28  0.00796
##  7 2013-07-31  0.0683 
##  8 2013-08-30  0.00797
##  9 2013-09-30  0.0816 
## 10 2013-10-31  0.0821 
## # 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    0.507

6 Plot: Skewness Comparison

#Data transformation: calculate sknewness
asset_skewness_tbl <- asset_returns_tbl %>%
  
  group_by(asset) %>%
  summarise(skew = skewness(returns)) %>%
  ungroup() %>%
  
# Plot skewness
add_row(tibble(asset = "Portfolio",
               skew = skewness(portfolio_returns_tbl$returns)))
asset_skewness_tbl
## # A tibble: 6 × 2
##   asset       skew
##   <chr>      <dbl>
## 1 AMZN      0.187 
## 2 GOOG      0.784 
## 3 META      1.15  
## 4 MSFT      0.0825
## 5 NVDA      0.899 
## 6 Portfolio 0.507
# 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

Compared to the portfolio, the META stock appears to be returning the highest amount. META is at a skewness of around 1.15, while the portfolio skewness is sitting around 0.5.