# 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("TSLA", "GM", "F", "VWAGY", "HMC")
prices <- tq_get(x = symbols, 
                    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] "F"     "GM"    "HMC"   "TSLA"  "VWAGY"
# weights
weight <- c(0.15, 0.25, 0.2, 0.3, 0.1)
weight
## [1] 0.15 0.25 0.20 0.30 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 F         0.15
## 2 GM        0.25
## 3 HMC       0.2 
## 4 TSLA      0.3 
## 5 VWAGY     0.1

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  0.0357
##  2 2013-02-28 -0.0476
##  3 2013-03-28  0.0340
##  4 2013-04-30  0.150 
##  5 2013-05-31  0.220 
##  6 2013-06-28  0.0120
##  7 2013-07-31  0.115 
##  8 2013-08-30  0.0404
##  9 2013-09-30  0.0750
## 10 2013-10-31 -0.0302
## # … with 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     2.23

6 Plot: Rolling kurtosis

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 = "purple",
             label = str_glue("Downside risk started increasing
                              at the beginning of 2016"))

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.

It is important to note that this portfolio had shown a negative skewness starting in 2016. Negative kurtosis is less risky to investors as there are less returns in the tails.More returns in the tails means that the portfolio is riskier. But as is shown in the graph, there were only two brief instances in 2015 where kurtosis was negative. The downside risk of this portfolio has increased over time starting in 2016.The negatively skewed distribution means that kurtosis is fat tails to the left, indicating downside risk, as large losses are more likely.