# 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("TSLA", "AMZN", "AAPL", "NVDA", "PG")

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" "AMZN" "NVDA" "PG"   "TSLA"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL        0.2
## 2 AMZN        0.2
## 3 NVDA        0.2
## 4 PG          0.2
## 5 TSLA        0.2

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  0.0226 
##  2 2013-02-28 -0.0105 
##  3 2013-03-28  0.0240 
##  4 2013-04-30  0.0760 
##  5 2013-05-31  0.146  
##  6 2013-06-28 -0.00567
##  7 2013-07-31  0.103  
##  8 2013-08-30  0.0473 
##  9 2013-09-30  0.0486 
## 10 2013-10-31  0.0208 
## # 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    0.067

6 Plot: Skewness Comparison

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.

# 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 NVDA       0.899 
## 4 PG         0.0728
## 5 TSLA       0.944 
## 6 Portfolio  0.0670
# 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")

Obeservation from Skewness Plot

Upon examining the skewness values of my individual assets in comparison to the portfolio skewness, several key points emerge:

Positive skewness is observed in AMZN, NVDA, PG, and TSLA, indicating return distributions with longer right tails. These assets have a higher probability of extreme positive returns, though not necessarily higher average returns.

NVDA and TSLA display the strongest positive skewness, suggesting greater potential for large upside movements, often linked to higher volatility and investor optimism.

AAPL exhibits negative skewness, meaning its returns are more prone to modest losses and have a longer left tail—indicating occasional sharp declines.

The portfolio’s skewness lies slightly above zero, showing a mildly positive bias overall, implying a balance between risk and opportunity with a slight tendency toward favorable outcomes.

asset_returns_tbl %>%
    
    ggplot(aes(x = returns)) +
    geom_density(aes(color = asset), show.legend = FALSE, alpha = 1) +
    geom_histogram(aes(fill = asset), show.legend = FALSE, alpha = 0.3, binwidth = 0.01) + 
    facet_wrap(~asset, ncol = 1, scales = "free_y")  + 
    
    # labeling
    labs(title = "Distribution of Monthly Returns, 2012-2016",
         y = "Frequency",
         x = "Rate of Returns",
         capition = "A typical monthly return is higher for SPY and IJS than for AGG, EEM, and EFA")

Observations from apply 4

A typical quarterly return is higher for AMZN and NVDA than for AAPL and PG. TSLA shows the widest spread with occasional very large gains, while PG is the most stable with the smallest, more modest returns.

Conclusion

Overall, the portfolio demonstrates a balanced risk–return profile. High-growth assets like AMZN, NVDA, and TSLA contribute positively to both average returns and right-tailed skewness, offering strong upside potential. Meanwhile, AAPL and PG provide stability and lower volatility, helping to smooth portfolio fluctuations. The combined effect is a portfolio with a slight positive skew, indicating modest asymmetry toward favorable returns while maintaining controlled downside risk.