# 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("AAPL", "NFLX", "SBUX", "DIS", "MSFT")
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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "DIS"  "MSFT" "NFLX" "SBUX"
# 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 AAPL       0.25
## 2 DIS        0.25
## 3 MSFT       0.2 
## 4 NFLX       0.2 
## 5 SBUX       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  0.107 
##  2 2013-02-28  0.0250
##  3 2013-03-28  0.0214
##  4 2013-04-30  0.0876
##  5 2013-05-31  0.0317
##  6 2013-06-28 -0.0435
##  7 2013-07-31  0.0604
##  8 2013-08-30  0.0450
##  9 2013-09-30  0.0343
## 10 2013-10-31  0.0644
## # ℹ 50 more rows

5 Calculate Standard Deviation

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   -0.311

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")

### Scatterplot of 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$returns)))

asset_skewness_tbl
## # A tibble: 6 × 2
##   asset        skew
##   <chr>       <dbl>
## 1 AAPL      -0.555 
## 2 DIS       -0.502 
## 3 MSFT       0.0825
## 4 NFLX       0.909 
## 5 SBUX      -0.320 
## 6 Portfolio -0.311
# 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.

Based on the graph I made the best two to invest in with the highest rate of returns would be Netflix and Microsoft. This makes since since Netflix is one of the worlds leading entertainment company and microsoft is a huge tech giant.