# Load packages

# Core
library(tidyverse)
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library(tidyquant)
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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", "GOOG")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()

w <- c(0.35,
       0.35,
       0.30)

w_tbl <- tibble(symbols, w)

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col   = asset,
                 returns_col  = returns,
                 weights      = w_tbl,
                 col_rename   = "returns",
                 rebalance_on = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.0231 
##  2 2013-02-28  0.0178 
##  3 2013-03-28  0.00654
##  4 2013-04-30  0.0570 
##  5 2013-05-31  0.0450 
##  6 2013-06-28 -0.0435 
##  7 2013-07-31  0.0247 
##  8 2013-08-30  0.0281 
##  9 2013-09-30  0.00311
## 10 2013-10-31  0.108  
## # ℹ 50 more rows

5 Compute Skewness

portfolio_returns_tbl %>%

    tq_performance(Ra = returns,
                   Rb = NULL,
                   performance_fun = table.Stats) %>%
    select(Skewness)
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1  -0.0357

6 Plot: Skewness Comparison

asset_returns_skew_tbl <- asset_returns_tbl %>%

    group_by(asset) %>%
    summarise(skew = skewness(returns)) %>%
    ungroup() %>%

    add_row(tibble(asset = "Portfolio",
                  skew = skewness(portfolio_returns_tbl$returns)))


asset_returns_skew_tbl %>%

    ggplot(aes(asset, skew, color = asset)) +
    geom_point() +

    ggrepel::geom_text_repel(aes(label = asset),
                             data = asset_returns_skew_tbl %>%
                                 filter(asset == "Portfolio"),
                             size = 5,
                             show.legend = FALSE) +
    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.
An asset in my portfolio that might have an extreme positive return compared to my portfolio is GOOG. GOOG is positively skewed to over .5 which can indicate a greater number of values that are lower than average, and a few values that are significantly above the average.