# 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("NVDA", "AAPL", "AMD", "GOOG", "INTC")
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" "AMD"  "GOOG" "INTC" "NVDA"
## [1] "NVDA" "AAPL" "AMD" "GOOG" "INTC" 
# 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
## [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 AMD        0.25
## 3 GOOG       0.2 
## 4 INTC       0.2 
## 5 NVDA       0.1

4 Build a 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 -0.00164
##  2 2013-02-28 -0.00108
##  3 2013-03-28  0.0152 
##  4 2013-04-30  0.0583 
##  5 2013-05-31  0.114  
##  6 2013-06-28 -0.0279 
##  7 2013-07-31  0.0103 
##  8 2013-08-30 -0.0323 
##  9 2013-09-30  0.0532 
## 10 2013-10-31  0.0333 
## # 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    0.338

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 AMD        0.293 
## 3 GOOG       0.784 
## 4 INTC      -0.0321
## 5 NVDA       0.899 
## 6 Portfolio  0.338
# Plot skewness
asset_skewness_tbl %>%
    
    ggplot(aes(x = asset, y = skew, color = asset)) + 
    geom_point() + 
    
     geom_text(aes(label = asset),
              vjust = 1.5,   
                             data = asset_skewness_tbl %>%
                                 filter(asset == "Portfolio")) +
    labs(y = "skewness")

## Scatterplot of skewness comparison

# Transform data: calculate rolling skewness
rolling_skew_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(Select     = returns, 
              mutate_fun = rollapply, 
              width      = 24,
              FUN        = skewness,
              col_rename = "Skew") %>%
    
    select(-returns) %>%
    na.omit()

# Plot
rolling_skew_tbl %>%
    
    ggplot(aes(x = date, y = Skew)) + 
    geom_line(color = "cornflowerblue") +
    
    geom_hline(yintercept = 0, linetype = "dotted", size = 2) +
    
    # Formatting
    scale_y_continuous(limits = c(-1,1), breaks = seq(-1,1,0.2)) +
    theme(plot.title = element_text(hjust = 0.5)) +

    # Labeling
    labs(y = "Skewness",
         x = NULL,
         title = "Rolling 24-Month Skewness") +

    annotate(geom = "text", 
             x = as.Date("2016-07-01"), y = 0.8, 
             color = "red", size = 5,
             label = str_glue("The 24-month skewness is positive for about half of the lifetime, 
                              even though the overall skewness is negative"))

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. Yes in my portfolio Nvidia and Google are both over 0.5 showing an extreme positive skewness and brings my portfolio up to around 0.25. This is offset by Apple having a negative skewness of 0.5 respectively balancing out the skewness if I only had Nvidia for instance and not both Nvidia and Google. This is reflected by the 24 month rolling skewness as the graph shows a high postive skewness with only slightly dipping into the negative skewed range in late 2015 and early 2016. As well as sipping in the beginning of 2018. Over all giving a positive return and a high return at that.