# 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("NVDA", "AAPL", "AMD", "GOOG", "INTC")
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" "AMD"  "GOOG" "INTC" "NVDA"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl 
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 AMD        0.25
## 3 GOOG       0.2 
## 4 INTC       0.2 
## 5 NVDA       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 -0.00164
##  2 2013-02-28 -0.00108
##  3 2013-03-28  0.0152 
##  4 2013-04-30  0.0583 
##  5 2013-05-31  0.114  
##  6 2013-06-28 -0.0279 
##  7 2013-07-31  0.0103 
##  8 2013-08-30 -0.0323 
##  9 2013-09-30  0.0532 
## 10 2013-10-31  0.0333 
## # 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    0.337

6 Plot: Rolling kurtosis

# Assign a 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)

# Plot
rolling_kurt_tbl %>%
    
    ggplot(aes(x = date, y = kurt)) +
    geom_line(color = "cornflowerblue") +
    
    #Formatting
    scale_y_continuous(breaks = seq(from = 0, to = 5, by = 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 = str_glue("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.
According to the kurtosis plot of the 24 month rolling kurtosis. There was a downwards slide beginning in 2015 which was followed by a steep upwards slope in late 2015 going into 2016. What this means for my portfolio is that the risk was higher in the beginning of the 24 month window but gradually increased to above the 0% threshold spiking in 2016 just shy of 0.5%. After this spike my portfolio saw another downturn in 2017 but not as drastic as the previously mentioned 2015 showing some stability in the risk return of the stocks in the portfolio. From 2017 on the curve shows steady growth peaking above the 2016 threshold and being just shy of 1%. What this means for my portfolio is that it has shown downside risk growing in the later stages of the kurtosis plot which is promising and if I graphed a longer time frame I believe we would see a similar pattern continuing until now.