# 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("WMT", "TGT", "GOOG", "AMZN", "LOW")

prices <- tq_get(x = symbols, 
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2025-12-31")

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] "AMZN" "GOOG" "LOW"  "TGT"  "WMT"
# 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 AMZN        0.2
## 2 GOOG        0.2
## 3 LOW         0.2
## 4 TGT         0.2
## 5 WMT         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: 156 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31  0.0490  
##  2 2013-02-28  0.0223  
##  3 2013-03-28  0.0278  
##  4 2013-04-30  0.0149  
##  5 2013-05-31  0.0328  
##  6 2013-06-28 -0.000319
##  7 2013-07-31  0.0519  
##  8 2013-08-30 -0.0522  
##  9 2013-09-30  0.0405  
## 10 2013-10-31  0.0826  
## # ℹ 146 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 = "2025-12-31") %>%
    
    # 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       0.989

6 Plot: Scatter with regression line

    ggplot(data = portfolio_market_returns_tbl,
       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?

This portfolio moves in the direction of the market. It has a positive beta at 0.989. This means that if the market increases by 1%, this portfolio will increase by 0.989%. The plot shows a positive slope, which further proves that the portfolio moves in a linear slope between returns of the portfolio, and that of the market.