# 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", "MSFT", "META", "TSLA", "NFLX")

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" "META" "MSFT" "NFLX" "TSLA"
#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 META       0.25
## 3 MSFT       0.2 
## 4 NFLX       0.2 
## 5 TSLA       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  0.131   
##  2 2013-02-28 -0.0158  
##  3 2013-03-28  0.000339
##  4 2013-04-30  0.112   
##  5 2013-05-31  0.0533  
##  6 2013-06-28 -0.0327  
##  7 2013-07-31  0.166   
##  8 2013-08-30  0.113   
##  9 2013-09-30  0.0734  
## 10 2013-10-31  0.0247  
## # … with 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   0.0436

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 META       1.15  
## 3 MSFT       0.0825
## 4 NFLX       0.909 
## 5 TSLA       0.944 
## 6 Portfolio  0.0436
# 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.

As I can identify in the graph, I would not say that there is an asset that would outperform this whole portfolio collectively in returns since many of the show great results. But the most skewed asset within the portfolio is META. To reassure that the positive skewness of META is accurate, I am comparing it towards the distribution chart. I can Identify that the typical return of META is above 0 and it has some huge returns by the looks of the bar on the far right. That indicates that the skewness is fair and that large positive gains are more likely to happen compared to the other assets. A skewness of approximately 1.2, proves that META is HIGLEY skewed.