# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(ggrepel)
library(scales)

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"))
##     asset      date   returns 
##   "asset"    "date" "returns"

3 Assign a weight to each asset

# symbols
symbols <- asset_returns_tbl %>% distinct(symbol) %>% pull()
    

# 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

portfolio_returns_tbl <- asset_returns_tbl %>%
    tq_portfolio(assets_col = symbol, 
                 returns_col = monthly.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 
## # … with 50 more rows

Calculate Kurtosis

portfolio_kurt_tiddyquant_builtin_percent <- portfolio_returns_tbl %>%

tq_performance(Ra = returns,
              performance_fun = table.Stats) %>%
    
    select(Kurtosis) 

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

Plot

Distribution of Portfolio Returns

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

Expected Retruns vs Downside Risk

#trasnform data
mean_kurt_tbl <- asset_returns_tbl %>%

# calculate mean returns and kurtosis
    group_by(symbol) %>%
    summarise(mean = mean(monthly.returns),
              kurt = kurtosis(monthly.returns)) %>%
    ungroup() %>% 
    # Add portfolio stats
    add_row(portfolio_returns_tbl %>%
    summarise(mean = mean(returns),
              kurt = kurtosis(returns)) %>%
    
mutate(symbol = "Portfolio"))


# Plot
mean_kurt_tbl %>%
    ggplot(aes(x = kurt, y = mean)) +
    geom_point() +
    ggrepel::geom_text_repel(aes(label = symbol, color = symbol)) +
    
    #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 for window
window = 24
# Transform Data 
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 
             towards the end of 2017")