# 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("TSLA", "NVDA", "AAPL", "MSFT", "AMZN")

prices <- tq_get(x    = symbols,
                 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] "AAPL" "AMZN" "MSFT" "NVDA" "TSLA"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 AMZN       0.25
## 3 MSFT       0.2 
## 4 NVDA       0.2 
## 5 TSLA       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: 155 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.00905
##  2 2013-02-28 -0.00315
##  3 2013-03-28  0.0196 
##  4 2013-04-30  0.0666 
##  5 2013-05-31  0.103  
##  6 2013-06-28 -0.0225 
##  7 2013-07-31  0.0651 
##  8 2013-08-30  0.0419 
##  9 2013-09-30  0.0447 
## 10 2013-10-31  0.0497 
## # ℹ 145 more rows

5 Calculate CAPM Beta

5.1 Get market returns

# Get market returns
market_returns_tbl <- tq_get("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

# Combine market returns with portfolio returns
portfolio_market_returns_tbl <- portfolio_returns_tbl %>%

    # Add market returns
    mutate(market_returns = market_returns_tbl %>% pull(returns))

5.3 CAPM Beta

portfolio_market_returns_tbl %>%

    tq_performance(Ra = returns,
                   Rb = market_returns,
                   performance_fun = CAPM.beta)
## # A tibble: 1 × 1
##   CAPM.beta.1
##         <dbl>
## 1        1.32

6 Plot: Scatter with regression line

portfolio_market_returns_tbl %>%

    ggplot(aes(market_returns, returns)) +
    geom_point(color = "cornflowerblue") +

    geom_smooth(method = "lm", se = FALSE,
                size = 1.5, color = tidyquant::palette_light()[3]) +

    labs(x = "market returns",
         y = "portfolio returns") +
    coord_cartesian(xlim = c(0,0.1), ylim = c(0,0.1))

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 portfolio is positive which correlates with the beta, although the actual returns are very volatile adn have a low correlation to the market.