# 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("NFLX", "AAPL", "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)

# symbols
symbols <- asset_returns_tbl%>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "NFLX" "TSLA"
# weights
weights <- c(.25, .25, .2)
weights
## [1] 0.25 0.25 0.20
w_tble <- tibble(symbols, weights)
w_tble
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 NFLX       0.25
## 3 TSLA       0.2

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col = asset, 
                 returns_col = returns,
                 weights = w_tble, 
                 rebalance_on ="months", 
                 col_rename = "Returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        Returns
##    <date>        <dbl>
##  1 2013-01-31  0.126  
##  2 2013-02-28  0.0111 
##  3 2013-03-28  0.0191 
##  4 2013-04-30  0.104  
##  5 2013-05-31  0.136  
##  6 2013-06-28 -0.0301 
##  7 2013-07-31  0.114  
##  8 2013-08-30  0.103  
##  9 2013-09-30  0.0429 
## 10 2013-10-31 -0.00445
## # … 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   -0.195

6 Plot: Rolling kurtosis

# Assign a value for window
window = 12


rolling_kurt_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select = Returns,
              mutate_fun = rollapply,
              width = window,
              FUN   = kurtosis,
              col_rename = "kurt") %>%
    
    na.omit()%>%
    select(-Returns)


rolling_kurt_tbl %>%
    
    ggplot(aes(x = date,
               y = kurt)) +
    geom_line(color = "cornflowerblue") +
    

    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)) +
    

    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 = "green",
             label = str_glue("Downside risk increased after 2014
                              but quickly stabilized after 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.

Downside risk has not really increased or decreased over time. The only time downside risk increased was at the end of 2014.