# 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

symbol <- c("SPY", "EFA", "IJS", "EEM", "AGG")

prices <- tq_get(x = symbol,
                 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 <- asset_returns_tbl %>% distinct(symbol) %>% pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
weight <- c(0.25,0.25,0.2,0.2,0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)

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")

5 Calculate Standard Deviation

portfolio_skew_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
    
    tq_performance             (Ra = returns, 
                   performance_fun = table.Stats) %>%
    select                     (Skewness)

portfolio_skew_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1   -0.168

6 Plot

Histograms of expected returns and risk

sd_portfolio <- sd(portfolio_returns_tbl$returns)
mean_portfolio <- mean(portfolio_returns_tbl$returns)

portfolio_returns_tbl %>%
    
    
    #Add a New Variable
    mutate(extreme_neg = ifelse(returns < mean_portfolio - 2 * sd_portfolio,
                                "ext_neg", 
                                "not_ext_neg")) %>%
    
    ggplot(aes(x = returns, fill = extreme_neg)) +
    geom_histogram(binwidth = 0.003) +
    
    scale_x_continuous(breaks = seq(-0.06, 0.06, 0.02)) +
    
    labs(x = "Monthly Returns")

Scatterplot of Skewness

asset_skewness_tbl <- asset_returns_tbl %>%
    
    group_by(symbol) %>%
    summarise(skew = skewness(monthly.returns)) %>%
    ungroup() %>%
    
    # Add Portfolio Skewness
    add_row(tibble(symbol = "portfolio",
                   skew = skewness(portfolio_returns_tbl$returns)))
asset_skewness_tbl
## # A tibble: 6 × 2
##   symbol       skew
##   <chr>       <dbl>
## 1 AGG       -0.599 
## 2 EEM       -0.0512
## 3 EFA       -0.142 
## 4 IJS        0.216 
## 5 SPY       -0.264 
## 6 portfolio -0.168
asset_skewness_tbl %>%
    
    ggplot(aes(x = symbol, 
               y= skew,
               color = symbol)) +
    geom_point() +
    
    ggrepel::geom_text_repel(aes(label = symbol),
                             data = asset_skewness_tbl %>%
                                 filter(symbol == "portfolio")) +
    labs(y = "skewness")

Rolling Skewness

rolling_skew_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select     = returns,
              mutate_fun = rollapply, 
              width      = 24,
              FUN        = skewness, 
              col_rename = "skew") %>%
    select(-returns) %>%
    na.omit()

# Plot
rolling_skew_tbl %>% 
    
    ggplot(aes(x = date, 
               y = skew)) +
    geom_line(color = "cornflowerblue") +
    
    geom_hline(yintercept = 0, linetype = "dotted", size = 2) +
    
    # Formatting 
    scale_y_continuous(limits = c(-1,1), breaks = seq(-1,1,0.2)) +
    theme(plot.title = element_text(hjust = 0.5)) +
    
    # Labeling 
    labs(y = "skewness",
         x = NULL,
         title = "Rolling 24-Month Skewness") +
        
    annotate(geom = "text",
             x = as.Date("2016-07-01"), y = 0.8, label = "The 24-Month rolling skewness is positive for about half of the lifetime, even though the overall skewness is negative")