# 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,
                  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
symbol <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "SPY" "EFA" "IJS" "EEM" "AGG"
# 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 SPY        0.25
## 2 EFA        0.25
## 3 IJS        0.2 
## 4 EEM        0.2 
## 5 AGG        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  0.0308  
##  2 2013-02-28 -0.000870
##  3 2013-03-28  0.0187  
##  4 2013-04-30  0.0206  
##  5 2013-05-31 -0.00535 
##  6 2013-06-28 -0.0229  
##  7 2013-07-31  0.0412  
##  8 2013-08-30 -0.0255  
##  9 2013-09-30  0.0544  
## 10 2013-10-31  0.0352  
## # … with 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    0.449

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 = str_glue("Downside Risk Skyrocketed 
                              Toward End of 2017"))