# 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", "NVDA", "GOOGL", "ORCL", "JNJ")
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] "GOOGL" "JNJ"   "NVDA"  "ORCL"  "TSLA"
# weights
weight <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 GOOGL     0.25
## 2 JNJ       0.25
## 3 NVDA      0.2 
## 4 ORCL      0.2 
## 5 TSLA      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  0.0527 
##  2 2013-02-28  0.0169 
##  3 2013-03-28  0.0146 
##  4 2013-04-30  0.0728 
##  5 2013-05-31  0.0888 
##  6 2013-06-28 -0.00817
##  7 2013-07-31  0.0626 
##  8 2013-08-30 -0.00442
##  9 2013-09-30  0.0415 
## 10 2013-10-31  0.0361 
## # 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   -0.585

6 Plot: Rolling kurtosis

# Assign 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)

# Pot
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 = "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 increased over time.

A higher kurtosis indicates a greater probability of significant deviations from the mean, making it more unpredictable and risky than a lower one.