# 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
weight <- c(0.25, 0.25, 0.20, 0.20, 0.10)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <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

library(PerformanceAnalytics)
?Return.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 Compute Kurtosis

portfolio_kurt_tidyquant_bulitin_percent <- portfolio_returns_tbl %>%

    tq_performance(Ra = returns, 
                   performance_fun = table.Stats) %>%
    
    select(Kurtosis)
#The portfolio have a kurtosis is smaller than 3, meaning it has thinner tail that normal distribution -> Less risky, more returns is in the center (more predictable)

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

6 Plot

Distribution. of Portfolio Retunrs

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

Expected Return vs Downside Risk

# Transform data 
mean_kurt_tbl <- asset_returns_tbl %>%
    
    # 1. 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)) +
    
    # Labels
    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(colour = "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 sky rocketed towards the end of 2017"))