# 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 = "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

# 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

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 value window 
window = 24

# Transform data: calculate Rolling 24 month 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 = str_glue("Downside Risk skyrocketed 
                            toward the end of 2017"))