# 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("NOK", "GOOGL", "GM", "FDX", "AMZN")

prices <- tq_get(x    = symbols, 
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2017-12-31")


## 2 Convert prices to returns (monthly)

``` r
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"  "FDX"   "GM"    "GOOGL" "NOK"
# 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 FDX        0.25
## 3 GM         0.2 
## 4 GOOGL      0.2 
## 5 NOK        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  0.0466 
##  2 2013-02-28  0.00621
##  3 2013-03-28 -0.0229 
##  4 2013-04-30  0.00803
##  5 2013-05-31  0.0525 
##  6 2013-06-28  0.0209 
##  7 2013-07-31  0.0602 
##  8 2013-08-30 -0.0349 
##  9 2013-09-30  0.111  
## 10 2013-10-31  0.126  
## # 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    0.171
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- 
mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] NA

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 AMZN       0.187  
## 2 FDX       -0.0655 
## 3 GM        -0.00490
## 4 GOOGL      0.867  
## 5 NOK        1.42   
## 6 portfolio  0.171
# 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") %>%
    na.omit()

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.

Thankfully, none of my asset’s in my portfolio are in the negatives, but all of them for the most part will have good positive returns. Especially Nokia funily enough. Nokia has the possibility to sky rocket if things go right for them. In a close but not so close second we have Google, which has a good skewness just under that 1 mark.