# 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

# Choose stocks

symbols <- c("SPY", "NVDA", "VOOG")


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

2 Convert prices to returns

 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

3 Assign a weight to each asset

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

weights <- c (.25, .50, .25)
weights
## [1] 0.25 0.50 0.25
weights_tbl <- tibble(symbols, weights)
weights_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 NVDA       0.25
## 2 SPY        0.5 
## 3 VOOG       0.25

4 Build a portfolio

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

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0347
##  2 2013-02-28  0.0193
##  3 2013-03-28  0.0310
##  4 2013-04-30  0.0322
##  5 2013-05-31  0.0311
##  6 2013-06-28 -0.0173
##  7 2013-07-31  0.0436
##  8 2013-08-30 -0.0150
##  9 2013-09-30  0.0384
## 10 2013-10-31  0.0286
## # ℹ 50 more rows

5 Calculate Skewness

portfolio_skew_tidyquant_building_percent <- portfolio_returns_tbl  %>%

    tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    select(Skewness)
portfolio_skew_tidyquant_building_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1    0.209

6 plot

Histogram of excpected returns and risk

6 Plot

Histogram of Expected Returns and Risk

#Calculate sd of portfolio returns
sd_portfolio <- sd(portfolio_returns_tbl$returns)
mean_portfolio <- mean(portfolio_returns_tbl$returns)

portfolio_returns_tbl %>%

    
    # Add a new variable
    mutate(extreme_neg = ifelse(returns < mean_portfolio - 2 * sd_portfolio, 
                                "ext_neg", 
                                "not_ext_neg")) %>%
    
    ggplot(aes(x = returns, fill = extreme_neg)) + 
    geom_histogram(binwidth = 0.003) + 
    
    scale_x_continuous(breaks = seq(-.06, 0.06,0.02)) +
    
    labs(x = "monthly returns")

# 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: 4 × 2
##   asset       skew
##   <chr>      <dbl>
## 1 NVDA       0.899
## 2 SPY       -0.264
## 3 VOOG      -0.117
## 4 Portfolio  0.209
#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")

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.
Long term I like the NVDA stock as the skewness is the highest and i think it has the greatest chance of going back to what it was before or even higher