# 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,
                replace_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 -0.00220
##  3 2013-03-28  0.0127 
##  4 2013-04-30  0.0173 
##  5 2013-05-31 -0.0113 
##  6 2013-06-28 -0.0233 
##  7 2013-07-31  0.0342 
##  8 2013-08-30 -0.0231 
##  9 2013-09-30  0.0513 
## 10 2013-10-31  0.0305 
## # ℹ 50 more rows

5 Caluculate 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    0.337
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <-
    mean(portfolio_returns_tbl$returns)

portfolio_kurt_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1    0.337

##6 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 Votality

# 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 = "Kurtois",
         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"))