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

2 Convert prices to returns (monthly)

asset_return_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_return_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "GOOG" "META" "NFLX" "TSLA" "XOM"
#weights
weights <- c(0.25, 0.20, 0.20, 0.20, 0.15)
weights
## [1] 0.25 0.20 0.20 0.20 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GOOG       0.25
## 2 META       0.2 
## 3 NFLX       0.2 
## 4 TSLA       0.2 
## 5 XOM        0.15

4 Build a portfolio

# ?tq_portfolio()

portfolio_returns_tbl <- asset_return_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 0.189   
##  2 2013-02-28 0.000283
##  3 2013-03-28 0.00420 
##  4 2013-04-30 0.121   
##  5 2013-05-31 0.119   
##  6 2013-06-28 0.0116  
##  7 2013-07-31 0.160   
##  8 2013-08-30 0.0772  
##  9 2013-09-30 0.0897  
## 10 2013-10-31 0.0171  
## # … 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    0.287

6 Plot: Rolling kurtosis

#assign a value for window
window = 24

#Transform Data: Calculate Rolling 24 Month Kurtosis
rolling_kurt_tbl <- portfolio_returns_tbl %>% 
    
    tq_mutate(seclect = 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"))

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 for my portfolio has both increased and decreased over time. In 2016 there was a very steep negative skew in my portfolio, but since then has slowly increased to the same level of Kurtosis as before.