# 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”, “AAPL”

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

1 Import stock prices

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

prices <- tq_get(x = symbols,
                 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] "AAPL" "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 AAPL       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,
               rebalence_on = "months",
               col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.0169 
##  2 2013-02-28 -0.00928
##  3 2013-03-28  0.0137 
##  4 2013-04-30  0.0157 
##  5 2013-05-31 -0.00218
##  6 2013-06-28 -0.0468 
##  7 2013-07-31  0.0611 
##  8 2013-08-30 -0.00531
##  9 2013-09-30  0.0446 
## 10 2013-10-31  0.0486 
## # ℹ 50 more rows

5 Calculate Standard Deviation

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   -0.266

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")
## $x
## [1] "monthly returns"
## 
## attr(,"class")
## [1] "labels"

Scatterplot of skewness comparison

#Data transformation: calculate sknewness
asset_skewness_tbl <- asset_returns_tbl %>%
  
  group_by(asset) %>%
  summarise(skew = skewness(returns)) %>%
  ungroup() %>%
  
# Plot 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 EEM       -0.0512
## 3 EFA       -0.142 
## 4 IJS        0.216 
## 5 SPY       -0.264 
## 6 Portfolio -0.266
# 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 sknewness

# 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)) +
  
  # Labelling
    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 rolling skewness is positive for about half of the lifetime, 
                             even though the overall skewness is negative"))