# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Calculate and visualize your portfolio’s beta.

Choose your stocks and the baseline market.

from 2012-12-31 to present

1 Import stock prices

symbols <- c("MCD", "WEN", "YUM", "DPZ", "SBUX")

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] "DPZ"  "MCD"  "SBUX" "WEN"  "YUM"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 DPZ         0.2
## 2 MCD         0.2
## 3 SBUX        0.2
## 4 WEN         0.2
## 5 YUM         0.2

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  0.0524 
##  2 2013-02-28  0.0273 
##  3 2013-03-28  0.0496 
##  4 2013-04-30  0.0226 
##  5 2013-05-31  0.0218 
##  6 2013-06-28  0.00976
##  7 2013-07-31  0.0804 
##  8 2013-08-30 -0.00594
##  9 2013-09-30  0.0690 
## 10 2013-10-31  0.00341
## # … with 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x    = "SPY",
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2017-12-31") %>%
    
    #Convert prices to returns
    tq_transmute(select     = adjusted,
                 mutate_fun = periodReturn,
                 period     = "monthly",
                 type       = "log",
                 col_rename = "returns") %>%
    
    slice(-1)

5.2 Join returns

portfolio_market_returns_tbl <- left_join(market_returns_tbl, portfolio_returns_tbl, by = "date") %>%
    
    set_names("date", "market_returns", "portfolio_returns")

5.3 CAPM Beta

portfolio_market_returns_tbl %>%
    
    tq_performance(Ra = portfolio_returns,
                   Rb = market_returns,
                   performance_fun = CAPM.beta)
## # A tibble: 1 × 1
##   CAPM.beta.1
##         <dbl>
## 1       0.709

6 Plot: Scatter with regression line

portfolio_market_returns_tbl %>%
    
    ggplot(aes(x = market_returns,
               y = portfolio_returns)) +
    geom_point(color = "violet") +
    geom_smooth(method = "lm", 
                se = FALSE, 
                size = 1.5,
                color = tidyquant::palette_light()[3]) +
    
    labs(y = "Portfolio Returns",
         x = "Market Returns")

How sensitive is your portfolio to the market? Discuss in terms of the beta coefficient. Does the plot confirm the beta coefficient you calculated?

My portfolio has a beta of .709. This indicates that my portfolio of fast food companies is ~30% less volatile than the overall market as measured by the S&P500 index, SPY. This makes sense because the 5 companies in my portfolio are concentrated in one area. They are each large, well-established food companies that have little risk of failure. Their returns have been relatively predictable from a historical standpoint, so there is not much risk associated with these investments. This is corroborated by the regression analysis although it is a minimal impact and difficult to see in graph form. Looking at the violet-colored points on the scatter plot, I can notice that they are more concentrated on the bottom, right half and top right half. This means that the market is returning positive returns while the portfolio is seeing negative returns. The heavier concentration in the top right section indicates that when the portfolio is earning positive returns, the market likely is too.