# 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("MTN", "AAPL", "NFLX", "DIS", "GE")

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] "AAPL" "DIS"  "GE"   "MTN"  "NFLX"
#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 GE         0.2 
## 4 MTN        0.2 
## 5 NFLX       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,
                replace_on = "months", 
                col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0461
##  2 2013-02-28  0.0360
##  3 2013-03-28  0.0340
##  4 2013-04-30  0.0329
##  5 2013-05-31  0.0330
##  6 2013-06-28 -0.0411
##  7 2013-07-31  0.0795
##  8 2013-08-30  0.0183
##  9 2013-09-30  0.0387
## 10 2013-10-31  0.0591
## # ℹ 50 more rows
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   -0.615
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <-
    mean(portfolio_returns_tbl$returns)

portfolio_skew_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1   -0.615

5 Compute Skewness

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))
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
labs(x = "monthly returns")
## $x
## [1] "monthly returns"
## 
## attr(,"class")
## [1] "labels"
# 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 GE        -0.333
## 4 MTN        0.297
## 5 NFLX       0.909
## 6 portfolio -0.615
# Plot Skewness
asset_skewness_tbl %>%
    
    ggplot(aes(x = asset, y = skew, color = asset)) +
    geom_point() +
    
    ggrepel::geom_text_repel(aes(label = asset)) + 
                             
                                 
    labs(y = "skewness") + theme(legend.position = "none")

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.

Netflix is the most likely asset in the portfolio to return extreme positive outcomes, but it comes with higher risk and volatility.