# 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("MSFT", "NVDA", "JPM")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "JPM"  "MSFT" "NVDA"
# weights
weights <- c(0.4, 0.3, 0.3)
weights
## [1] 0.4 0.3 0.3
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 JPM         0.4
## 2 MSFT        0.3
## 3 NVDA        0.3

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: 132 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31  0.0380  
##  2 2013-02-28  0.0333  
##  3 2013-03-28  0.000498
##  4 2013-04-30  0.0803  
##  5 2013-05-31  0.0775  
##  6 2013-06-28 -0.0256  
##  7 2013-07-31  0.00859 
##  8 2013-08-30 -0.0148  
##  9 2013-09-30  0.0240  
## 10 2013-10-31  0.0132  
## # ℹ 122 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") %>%
   # 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        1.25

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?

With a beta of 1.25, my portfolio is more volatile than the market. You can see this clearly on my scatterplot with regression line.