# 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("NKE", "NFLX", "AMZN", "AAPL", "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", "data", "returns"))

3 Assign a weight to each asset (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols 
## [1] "AAPL" "AMZN" "MSFT" "NFLX" "NKE"
# weights 
weights <- c(0.25, 0.25, 0.20, 0.20, 0.10)
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 AMZN       0.25
## 3 MSFT       0.2 
## 4 NFLX       0.2 
## 5 NKE        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
##    data       returns
##    <date>       <dbl>
##  1 2013-01-31  0.101 
##  2 2013-02-28  0.0237
##  3 2013-03-28  0.0178
##  4 2013-04-30  0.0510
##  5 2013-05-31  0.0387
##  6 2013-06-28 -0.0364
##  7 2013-07-31  0.0652
##  8 2013-08-30  0.0438
##  9 2013-09-30  0.0521
## 10 2013-10-31  0.0861
## # 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   -0.179

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$returns)))

asset_skewness_tbl
## # A tibble: 6 × 2
##   asset        skew
##   <chr>       <dbl>
## 1 AAPL      -0.555 
## 2 AMZN       0.187 
## 3 MSFT       0.0825
## 4 NFLX       0.909 
## 5 NKE        0.0783
## 6 Portfolio -0.179
# 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.
The only asset in my portfolio that will likely have extreme positive returns comparatively to the portfolio as a whole would be Netflix(NFLX) which is around 0.909 skewness which is very good, meaning that more likley than not the return will be greater than the mean. The portfolio itself is at -0.179 which isn’t good, as you want to have a positive skewness because there is a probability that you can regain all the small profits you lost from a big gain. Apple(AAPL) would be the asset with the lowest skewness at -0.555 which isnt good at all, where as Amazon(AMZN), Microsoft(MSFT), and Nike(NKE) are all approximately 0.1 skewness which isn’t extremely desireable as its not much above 0. If I was to invest into one of my assets it would be Netflix based on this graph, as it has by far the highest skewness and I would stay away from Apple as it has an extremely low skewness.