# 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

5 Calculate Kurtosis

portfolio_kurt_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
  
  tq_performance(Ra = returns, 
                 performance_fun = table.Stats) %>%
  
  select(Kurtosis)
  

portfolio_kurt_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1    0.488

6 Plot

Distribution of Portfolio Returns

portfolio_returns_tbl %>%
  
  ggplot(aes(x = returns)) +
  geom_histogram()

Expected Return vs Downside Risk

# Transform data
mean_kurt_tbl <- asset_returns_tbl %>%
  
  # Calculate mean return and kurtosis for assets
  group_by(asset) %>% 
  summarise(mean = mean(returns),
            kurt = kurtosis(returns)) %>%
  ungroup() %>%
  
  # Add portfolio stats
  add_row(portfolio_returns_tbl %>%
  summarise(mean = mean(returns), 
            kurt = kurtosis(returns)) %>%
  mutate(asset = "Portfolio"))

# Plot
mean_kurt_tbl %>%
  
  ggplot(aes(x = kurt, y = mean)) +
  geom_point() +
  ggrepel::geom_text_repel(aes(label = asset, color = asset)) +
  
  # Formatting
  theme(legend.position = "none") +
  scale_y_continuous(labels = scales::percent_format(accuracy =0.1)) +
  
  # Labeling
  labs(x = "Kurtosis",
       y = "Expected Returns")

Rolling 24 Month Kurtosis

# Assign a value for window
window = 24
# Transform data: calculate 24 month rolling kurtosis
rolling_kurt_tbl <- portfolio_returns_tbl %>%
  
  tq_mutate(select = returns, 
            mutate_fun = rollapply, 
            width = window,
            FUN = kurtosis, 
            col_rename = "kurt") %>%
  
  na.omit() %>%
  select(-returns)

# Plot
rolling_kurt_tbl %>%
  
  ggplot(aes(x = date, y = kurt)) + 
  geom_line(color = "cornflowerblue") +
  
  # Formatting
  scale_y_continuous(breaks = seq(-1, 4, 0.5)) +
  scale_x_date(breaks = scales::pretty_breaks(n = 7)) +
  theme(plot.title = element_text(hjust = 0.5)) +
  
  # Labeling
  labs(x = NULL, 
       y = "Kurtosis", 
       title = paste0("Rolling ", window, " Month Kurtosis")) +
  
  annotate(geom = "text", 
           x = as.Date("2016-07-01"), 
           y = 3,
           size = 5, color = "red",
           label = "Downside risk skyrocketed
           toward the end of 2017")