# 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("JPM", "MS", "DNB.OL", "NDA-FI.HE")

prices <- tq_get(x    = symbols, 
                 get  = "stock.prices", 
                 from = "2017-01-01", 
                 to   = "2023-12-31") %>%
          filter(!is.na(close))

2 Convert prices to returns (monthly)

asset_returns_tbl <-prices %>%
    
    group_by(symbol) %>%
    
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "quarterly",
                 type = "log") %>%
    ungroup() %>%
    
    set_names(c("asset", "date", "returns"))
asset_returns_tbl
## # A tibble: 112 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 JPM   2017-03-31  0.0125
##  2 JPM   2017-06-30  0.0455
##  3 JPM   2017-09-29  0.0495
##  4 JPM   2017-12-29  0.119 
##  5 JPM   2018-03-29  0.0331
##  6 JPM   2018-06-29 -0.0488
##  7 JPM   2018-09-28  0.0851
##  8 JPM   2018-12-31 -0.138 
##  9 JPM   2019-03-29  0.0444
## 10 JPM   2019-06-28  0.107 
## # ℹ 102 more rows

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

#symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "JPM"       "MS"        "DNB.OL"    "NDA-FI.HE"
#weights 
weights <- c(0.25, 0.25, 0.25, 0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols   weights
##   <chr>       <dbl>
## 1 JPM          0.25
## 2 MS           0.25
## 3 DNB.OL       0.25
## 4 NDA-FI.HE    0.25

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")

portfolio_returns_tbl
## # A tibble: 33 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2017-03-31         0.0324   
##  2 2017-06-30         0.0531   
##  3 2017-09-29         0.0704   
##  4 2017-12-29         0.00773  
##  5 2018-03-28         0.0000821
##  6 2018-03-29        -0.00540  
##  7 2018-06-29        -0.0306   
##  8 2018-09-28         0.0706   
##  9 2018-12-28        -0.119    
## 10 2018-12-31        -0.0829   
## # ℹ 23 more rows

5 Compute Skewness

portfolio_returns_tbl %>%

    tq_performance(Ra = portfolio.returns,
                   Rb = NULL,
                   performance_fun = table.Stats) %>%
    select(Skewness)
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1    -1.82

6 Plot: Skewness Comparison

# Calculate skewness for each asset and add the portfolio skewness
asset_returns_skew_tbl <- asset_returns_tbl %>%
    group_by(asset) %>%
    summarise(skew = skewness(returns, na.rm = TRUE)) %>%
    ungroup() %>%
    add_row(
        asset = "Portfolio", 
        skew = skewness(pull(portfolio_returns_tbl, portfolio.returns), na.rm = TRUE)
    )

# Plot the skewness
asset_returns_skew_tbl %>%
    ggplot(aes(x = asset, y = skew, color = asset)) +
    geom_point(size = 3) +
    ggrepel::geom_text_repel(
        aes(label = asset),
        data = asset_returns_skew_tbl %>% filter(asset == "Portfolio"),
        size = 5
    ) +
    labs(title = "Asset and Portfolio Skewness", 
         x = "Asset", 
         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.

The skewness plot shows that MS is the only asset with positive skewness, indicating a higher likelihood of extreme positive returns compared to both the other assets and the portfolio. In contrast, the portfolio has the most negative skewness (around -1.5), suggesting it is more prone to extreme negative returns than any individual asset. This implies that while diversification may have reduced the chance of significant positive outcomes, it has also potentially increased exposure to extreme negative events.

Overall, the distribution of returns suggests that MS offers the greatest potential for outlier positive performance. Meanwhile, assets like JPM, DNB.OL, and NDA-FI.HE exhibit negative skewness but are less negatively skewed than the portfolio, indicating that the portfolio collectively faces a higher downside risk than these individual holdings