# 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,
               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 Calculate Standard Deviation

portfolio_skew_tidyquant_builtin_percent <- portfolio_returns_tbl %>%

tq_performance(Ra = returns, 
                performance_fun = table.Stats) %>%
  
  select(Skewness) %>%
  
  mutate(tq_sd = round(Skewness, 4))

portfolio_skew_tidyquant_builtin_percent
## # A tibble: 1 × 2
##   Skewness  tq_sd
##      <dbl>  <dbl>
## 1   -0.168 -0.168

6 Plot

Historgram Of Returns vs Risk

# Calculate sd of portfolio
sd_portfolio <- sd(portfolio_returns_tbl$returns)
mean_portfolio <- sd(portfolio_returns_tbl$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(bindwidth = 0.003) + 
  
  scale_x_continuous(breaks = seq(-0.6, 0.6, 0.02)) +

labs(x = "monthly returns")

Scatterplot of Skewness

# Data transformation: calculate skewness
asset_skewness_tbl <- asset_returns_tbl %>%
  
  group_by(asset) %>%
  summarise(skew = skewness(returns)) %>%
  ungroup() %>%
  
# Add portfolio skewness
  add_row(tibble(asset = "Portfolio",
                 skew = skewness(portfolio_returns_tbl$returns)))

asset_skewness_tbl
## # A tibble: 6 × 2
##   asset        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
# Plot Skewness
asset_skewness_tbl %>%
    
    ggplot(aes(x = asset, y = skew, color = asset)) +
    geom_point() + 
    
    ggrepel::geom_text_repel(aes(label = asset), 
                             data = asset_skewness_tbl %>%
                               filter(asset == "Portfolio")) +
  labs(y = "skewness")

Rolling Skewness

# Transform data: calculate 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)) +
                         
    #Labelling
    labs(y = "skewness", 
         x = NULL,
         title = "Rolling 24 Month Skewness") +
      
    annotate(geom = "text", 
             x = as.Date("2016-07-01"), 
             y = 0.8,
             color = "Red", size = 4,
             label = str_glue("The 24 Month rolling skewness is posative for half of it's lifetime, even though the overall skew is negative"))