# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(ggrepel)

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("UAL", "LUV", "AAL", "DAL")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31")

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] "AAL" "DAL" "LUV" "UAL"
# weights
weights <- c(0.2, 0.3, 0.3, 0.2)
weights
## [1] 0.2 0.3 0.3 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAL         0.2
## 2 DAL         0.3
## 3 LUV         0.3
## 4 UAL         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: 150 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0920
##  2 2013-02-28  0.0288
##  3 2013-03-28  0.169 
##  4 2013-04-30  0.0171
##  5 2013-05-31  0.0337
##  6 2013-06-28 -0.0370
##  7 2013-07-31  0.113 
##  8 2013-08-30 -0.120 
##  9 2013-09-30  0.139 
## 10 2013-10-31  0.133 
## # ℹ 140 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 monthly 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        1.29

6 Plot: Scatter with regression line

portfolio_market_returns_tbl %>%
    
    ggplot(aes(x = market_returns,
               y = portfolio_returns,)) +
    geom_point(color = "blue") +
    geom_smooth(method = "lm", se = FALSE, 
                linewidth = 1.5, color =
                    tidyquant::palette_light()[12]) +
    
    labs(x = "market returns",
         y = "portfolio 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?

The beta coefficient for this portfolio is 1.29, meaning that it is sensitive to market volatility. If the market return fluctuates by 1%, the portfolio would reflect that plus an extra 0.29% on both ends. There is not a very strong linear relationship due to how the stocks do not follow the regression line as closely. This implies that other outside factors can influence the returns of the portfolio