# 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("AAPL", "TSLA", "NFLX", "MTN", "DIS")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "DIS"  "MTN"  "NFLX" "TSLA"
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 AAPL      0.25
## 2 DIS       0.25
## 3 MTN       0.2 
## 4 NFLX      0.2 
## 5 TSLA      0.1

4 Build a 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  0.102 
##  2 2013-02-28  0.0242
##  3 2013-03-28  0.0451
##  4 2013-04-30  0.0806
##  5 2013-05-31  0.0871
##  6 2013-06-28 -0.0431
##  7 2013-07-31  0.108 
##  8 2013-08-30  0.0608
##  9 2013-09-30  0.0437
## 10 2013-10-31  0.0315
## # ℹ 50 more rows

5 Compute kurtosis

portfolio_skew_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = returns,
                   Rb = NULL,
                   performance_fun = table.Stats) %>%
    
    select(Kurtosis) 

portfolio_skew_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1  -0.0062

6 Plot: Rolling kurtosis

window <- 24

port_rolling_kurtosis_tbl <- portfolio_returns_tbl %>%

    tq_mutate(select = returns,
              mutate_fun = rollapply,
              width      = window,
              FUN        = kurtosis,
              col_rename = "rolling_kurtosis") %>%
    select(date, rolling_kurtosis) %>%
    na.omit()

port_rolling_kurtosis_tbl %>%

    ggplot(aes(date, rolling_kurtosis)) +
    geom_line(color = "cornflowerblue") +

    scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
    scale_x_date(breaks = scales::breaks_pretty(n = 7)) +

    labs(title = paste0("Rolling ", window, "-Month Kurtosis"),
         x = NULL,
         y = "kurtosis") +
    theme(plot.title = element_text(hjust = 0.5)) 

port_rolling_kurtosis_tbl
## # A tibble: 37 × 2
##    date       rolling_kurtosis
##    <date>                <dbl>
##  1 2014-12-31           -1.01 
##  2 2015-01-30           -0.882
##  3 2015-02-27           -0.937
##  4 2015-03-31           -1.07 
##  5 2015-04-30           -1.06 
##  6 2015-05-29           -0.973
##  7 2015-06-30           -0.762
##  8 2015-07-31           -0.679
##  9 2015-08-31           -0.644
## 10 2015-09-30           -0.882
## # ℹ 27 more rows

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.

It has increased over time, The kurtosis rises as you can see in the graph meaning that the returns are going to be further away from the mean. This could be good if they are positive but there is inherently more risk.