Apply 8

# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Collect individual returns into a portfolio by assigning a weight to each stock

two stocks: “TSLA”, “DELL”

from 2012-12-31 to 2017-12-31

1 Import stock prices

symbols <- c("TSLA", "DELL")

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns (monthly)

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 (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols

## [1] "DELL" "TSLA"

# weights
weights <- c(0.50,0.50)
weights

## [1] 0.5 0.5

w_tbl <- tibble(symbols, weights)
w_tbl

## # A tibble: 2 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 DELL        0.5
## 2 TSLA        0.5

4 Build a portfolio

#?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col   = asset,
                 returns_col  = returns,
                 weights      = w_tbl,
                 col_rename   = "returns",
                 rebalance_on = "months")

portfolio_returns_tbl

## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0510
##  2 2013-02-28 -0.0371
##  3 2013-03-28  0.0421
##  4 2013-04-30  0.177 
##  5 2013-05-31  0.297 
##  6 2013-06-28  0.0468
##  7 2013-07-31  0.112 
##  8 2013-08-30  0.115 
##  9 2013-09-30  0.0674
## 10 2013-10-31 -0.0949
## # 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     1.57

# 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)) +
    
# Labelling 
    labs(x = NULL,
         y = "Kurtosis",
         title = paste0("Rolling ", window, " Month Kurtosis")) +
    
    annotate(geom = "text", x = as.Date("2016-7-01"), y = 3, 
             size = 5, color = "red",
             label = str_glue("Downside risk stayed consistent
                              toward the end of 2017"))

Plot: Rolling kurtosis

The downside risk of my portfolio has decreased over time. The rolling kurtosis started at 0.05 and ended at -0.01. 3 of my 5 stocks as well as my portfolio have below 0 kurtosis on my plot meaning low price fluctuations. All of my stocks have a positive expected return.