# 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

# Choose stocks

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

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 %>%

# Calculate monthly returns
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"
# weight
weights <- c(0.25, 
0.25, 
0.20, 
0.20, 
0.10)
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

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 Kurtosis

portfolio_kurt_tidyquant_builtin_percent <- portfolio_returns_tbl %>%

tq_performance(Ra = returns,
performance_fun = table.Stats) %>%

select(Kurtosis)

portfolio_kurt_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1    0.488

6 Plot

Distribution of portfolio returns

portfolio_returns_tbl %>%

ggplot(aes(x = returns)) +
geom_histogram()

Expected Return vs Downside Risk

# 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"))
## # A tibble: 2 × 3
##       mean   kurt asset    
##      <dbl>  <dbl> <chr>    
## 1  0.00590  0.488 Portfolio
## 2 NA       NA     <NA>
# 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")

# 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"))