# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Collect individual returns into a portfolio by assigning a weight to each stock

five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”

from 2012-12-31 to 2017-12-31

1 Import stock prices

symbols <- c ("SPY", "EFA", "IJS", "EEM", "AGG")

prices <- tq_get(x = symbols,
                 get = "stockprices",
                 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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
# 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
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AGG        0.25
## 2 EEM        0.25
## 3 EFA        0.2 
## 4 IJS        0.2 
## 5 SPY        0.1

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  0.0204 
##  2 2013-02-28 -0.00239
##  3 2013-03-28  0.0121 
##  4 2013-04-30  0.0174 
##  5 2013-05-31 -0.0128 
##  6 2013-06-28 -0.0247 
##  7 2013-07-31  0.0321 
##  8 2013-08-30 -0.0224 
##  9 2013-09-30  0.0511 
## 10 2013-10-31  0.0301 
## # ℹ 50 more rows

Calculate 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   -0.168

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(-0.06, 0.06, 0.02)) +
  
  labs(x = "monthly returns")

Scatterplot of 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)))



# 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")

Rolling Skewness

# 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 = 4,
          label = str_glue("The 24 month rolling skewness is positive for about half of the lifetime, 
                           even though the overall skewness is negative"))