# 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("META", "MSFT", "GOOG", "NVDA", "AMZN")

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] "AMZN" "GOOG" "META" "MSFT" "NVDA"
#weights
weights <- c(0.5)
weights
## [1] 0.5
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN        0.5
## 2 GOOG        0.5
## 3 META        0.5
## 4 MSFT        0.5
## 5 NVDA        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,
               rebalence_on = "months",
               col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.151  
##  2 2013-02-28 -0.0133 
##  3 2013-03-28 -0.00909
##  4 2013-04-30  0.133  
##  5 2013-05-31  0.0491 
##  6 2013-06-28  0.00582
##  7 2013-07-31  0.141  
##  8 2013-08-30  0.0324 
##  9 2013-09-30  0.156  
## 10 2013-10-31  0.118  
## # 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   -0.506

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 = 3,
           size = 5, color = "red",
           label = str_glue("Downside risk skyrocketed
                            toward the end of 2015"))

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 went up towards the end of 2016, and started slowly rising again in 2017 and 2018. It started a downward trend towards the end of 2018 however. The portfolio has a kurtosis of -0.468 which means that it does not have as many extreme values. The skewness of the portfolio was 0.508 which suggests that there are higher amounts of extreme outliers in the portfolio.