# 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("IVV", "VOO", "VTSAX", "VSMPX", "FBGRX")
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] "FBGRX" "IVV"   "VOO"   "VSMPX" "VTSAX"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 FBGRX       0.2
## 2 IVV         0.2
## 3 VOO         0.2
## 4 VSMPX       0.2
## 5 VTSAX       0.2

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  0.0395
##  2 2013-02-28  0.0103
##  3 2013-03-28  0.0282
##  4 2013-04-30  0.0143
##  5 2013-05-31  0.0230
##  6 2013-06-28 -0.0126
##  7 2013-07-31  0.0452
##  8 2013-08-30 -0.0204
##  9 2013-09-30  0.0297
## 10 2013-10-31  0.0345
## # ℹ 50 more rows

5 Compute skewness

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

portfolio_skew_tidyqaunt_builtin_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1   -0.424

6 plot

Histogram of Expected Returns and Risk

# Caqlculate sd of portfolio returns
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(alpha = .7,
                   binwidth = 0.003) +
    
    scale_x_continuous(breaks = seq(-0.06, 0.06, 0.02)) +
  
    
    labs(x = "monthly returns")

Scatterplot of skewness comparison

# 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 = "porftolio",
                   skew = skewness(portfolio_returns_tbl$returns)))

asset_skewness_tbl
## # A tibble: 6 × 2
##   asset       skew
##   <chr>      <dbl>
## 1 FBGRX     -0.561
## 2 IVV       -0.293
## 3 VOO       -0.256
## 4 VSMPX     -0.274
## 5 VTSAX     -0.295
## 6 porftolio -0.423
# 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

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)) +
        
        # labeling
        labs(y = "skewness",
             x = NULL,
             title = "Rolling 24-Month Skewness") +
        
    annotate(geom = "text", x = as.Date("2016-07-01"), y = 0.8, color = "red", 
             label = str_glue("The 24 month rolling skewness is negative for about the entire lifetime, 
                              the overall skewness is negative"))