# 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(0.1, 0.4, 0.2, 0.15, 0.15)
weights
## [1] 0.10 0.40 0.20 0.15 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 FBGRX      0.1 
## 2 IVV        0.4 
## 3 VOO        0.2 
## 4 VSMPX      0.15
## 5 VTSAX      0.15

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  0.0424
##  2 2013-02-28  0.0111
##  3 2013-03-28  0.0304
##  4 2013-04-30  0.0159
##  5 2013-05-31  0.0221
##  6 2013-06-28 -0.0132
##  7 2013-07-31  0.0462
##  8 2013-08-30 -0.0240
##  9 2013-09-30  0.0295
## 10 2013-10-31  0.0372
## # ℹ 50 more rows

5 Calculate Kurtosis

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

portfolio_kurt_tidyqaunt_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1    0.860

6 Plot

Distribution of Portfolio Returns

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

Expected Returns vs Downside Risks

# Transform data
mean_kurt_tbl <- asset_returns_tbl %>%
    
    # 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))

    # Labeling
    labs(x = "Kurtosis",
         y = "Expected Returns")
## $x
## [1] "Kurtosis"
## 
## $y
## [1] "Expected Returns"
## 
## attr(,"class")
## [1] "labels"

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(color = "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 skyrocketed
                         toward the end of 2017"))