# 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("FDX", "UPS", "MSFT")
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"))
##     asset      date   returns 
##   "asset"    "date" "returns"

3 Assign a weight to each asset (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(symbol) %>% pull()
    

# weights
weights <- c(0.5, 0.25, 0.25)
weights
## [1] 0.50 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 FDX        0.5 
## 2 MSFT       0.25
## 3 UPS        0.25

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    tq_portfolio(assets_col = symbol, 
                 returns_col = monthly.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  0.0754
##  2 2013-02-28  0.0367
##  3 2013-03-28 -0.0180
##  4 2013-04-30  0.0145
##  5 2013-05-31  0.0291
##  6 2013-06-28  0.0114
##  7 2013-07-31  0.0169
##  8 2013-08-30  0.0183
##  9 2013-09-30  0.0466
## 10 2013-10-31  0.103 
## # … with 50 more rows

5 Compute kurtosis

portfolio_kurt_tiddyquant_builtin_percent <- portfolio_returns_tbl %>%

tq_performance(Ra = returns,
              performance_fun = table.Stats) %>%
    
    select(Kurtosis) 

portfolio_kurt_tiddyquant_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1    0.553

6 Plot: Rolling kurtosis

# Assign value for window
window = 24
# Transform Data 
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 = "Downside risk rises at the 
             end of the portfolio time frame")

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 rises pretty constantly as time goes on. At the end of the time frame the downside risk was significantly higher.