# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Visualize and examine changes in the underlying trend in the downside risk of your portfolio in terms of kurtosis.

Choose your stocks.

from 2012-12-31 to present

1 Import stock prices

symbols <- c("AMZN", "MSFT", "TSLA")
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)

# weights
weights <- c(0.3, 0.4, 0.3)
weights
## [1] 0.3 0.4 0.3
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN        0.3
## 2 MSFT        0.4
## 3 TSLA        0.3

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  0.0586
##  2 2013-02-28 -0.0153
##  3 2013-03-28  0.0393
##  4 2013-04-30  0.150 
##  5 2013-05-31  0.220 
##  6 2013-06-28  0.0333
##  7 2013-07-31  0.0590
##  8 2013-08-30  0.0701
##  9 2013-09-30  0.0710
## 10 2013-10-31  0.0135
## # ℹ 50 more rows

5 Compute 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    0.890

6 Plot: Rolling 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     = 1.5,
             size  = 5,
             color = "Blue",
             label = str_glue("Downside risk skyrocketed toward the end of 2017"))

Has the downside risk of your portfolio increased or decreased over time? Explain using the plot you created. You may also refer to the skewness of the returns distribution you plotted in the previous assignment.

The downside risk has definitely decreased from 2015 to 2017 but only slightly. The risk was higher at the beginning of 2015 and by mid 2016 it had fallen tremendously having a sharp drop mid 2015. However, it had slowly risen from 2016 to 2018 to almost where it was in the beginning of 2015.