# 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("AMZN", "TSLA", "RGR","WMT")
prices <- tq_get(x    = symbols, 
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2024-11-13")

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] "AMZN" "RGR"  "TSLA" "WMT"
# weights
weights <- c(0.25, 0.25, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.25
## 2 RGR        0.25
## 3 TSLA       0.2 
## 4 WMT        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: 143 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31 0.0650 
##  2 2013-02-28 0.00342
##  3 2013-03-28 0.00850
##  4 2013-04-30 0.0651 
##  5 2013-05-31 0.128  
##  6 2013-06-28 0.0146 
##  7 2013-07-31 0.0839 
##  8 2013-08-30 0.0328 
##  9 2013-09-30 0.0997 
## 10 2013-10-31 0.0146 
## # ℹ 133 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   = "2024-11-13") %>%
    
    # Convert prices to return
    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       0.769

6 Plot: Scatter with regression line

portfolio_market_returns_tbl %>%
    
    ggplot(aes(x = market_returns,
               y = portfolio_returns)) +
    geom_point(color = "cornflowerblue") +
    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?

When looking at the plot, we can see that it is upward sloping confirming that the Beta is positive. Also, the lines on the scatterplot are very scattered, showing that there isn’t a good relationship between the beta coefficent and the returns. This portfolios CAPM Beta is roughly .77, which means it brings in a return of .77% to every 1% of the market.