# 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,
                 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(0.25,0.25,0.2,0.2,0.1)
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
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 <- portfolio_returns_tbl %>%
    tq_performance(Ra = returns, 
                   performance_fun = table.Stats) %>%
    select(Skewness) 
portfolio_skew
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1   -0.168

6 Plot

Histogram

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)) +
    geom_histogram(binwidth = 0.003) +
    
    scale_x_continuous(breaks = seq(-0.06, 0.06, 0.02)) +
    labs(x="monthly returns")

Scatter Plot of Skewness

# Calculate Skewness
 skew_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)))

# Plot Skewness
skew_tbl %>%
    ggplot(aes(x=asset,y=skew,color=asset)) +
    geom_point() +

    ggrepel :: geom_text_repel(aes(label=asset),
                               data = skew_tbl %>%
                                   filter(asset == "Portfolio")) +
    labs(y = "skewness")

Rolling Skewness

#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()
rolling_skew_tbl
## # A tibble: 37 × 2
##    date         Skew
##    <date>      <dbl>
##  1 2014-12-31 -0.402
##  2 2015-01-30 -0.264
##  3 2015-02-27 -0.335
##  4 2015-03-31 -0.247
##  5 2015-04-30 -0.255
##  6 2015-05-29 -0.271
##  7 2015-06-30 -0.272
##  8 2015-07-31 -0.122
##  9 2015-08-31 -0.398
## 10 2015-09-30 -0.477
## # ℹ 27 more rows
    # 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)) +                      
        
        labs(y = "skewness",
             x = NULL,
             title = "Rolling 24 Month Skewness") +
            
            annotate(geom = "text",
                     x = as.Date("2016-07-01"),y = 0.8,
                     size = 2,
                     label = "the 24 month rolling skewness is positive for roughly half the lifetime, even though the overall skewness is negative")