# 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("XOM", "QQQ", "SPY", "TSLA","CGC")

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] "CGC"  "QQQ"  "SPY"  "TSLA" "XOM"
# 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 CGC        0.25
## 2 QQQ        0.25
## 3 SPY        0.2 
## 4 TSLA       0.2 
## 5 XOM        0.1

4 Build a portfolio

# ?tq_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  0.0409 
##  2 2013-02-28 -0.0113 
##  3 2013-03-28  0.0324 
##  4 2013-04-30  0.0796 
##  5 2013-05-31  0.135  
##  6 2013-06-28  0.00985
##  7 2013-07-31  0.0738 
##  8 2013-08-30  0.0323 
##  9 2013-09-30  0.0437 
## 10 2013-10-31 -0.0127 
## # 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   0.0092

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 = "steelblue") +
    
    # Formatting
    scale_y_continuous(breaks = seq(-1, 11, 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 = "orangered3",
             label = str_glue("Downside risk jumped significantly in Q4 of 15, then steadily
                    increasing, with a jump and fall in Q3 and Q4 of '17"))

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 of my portfolio has increased significantly over time. Base on the data from my graph of “Rolling 24 Month Kurtosis” At midway through 2015 it was at -1.25 to increasing to more than positive 0.5 by the end of 2017 with it reaching a height of 1.25 in Q3 of 2017. I believe this was the result of various positive and negative returns with an overall balance portfolio. This is overall very simular to the skewness experiences in Apply 7 which has to do with how I had positive skewness during the same period and must be based on market returns during the same period of time receiving positive returns.