# 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("SPY", "GOOG", "IBM", "QQQ", "TSLA")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",    
                 from = "2012-12-31",
                 to   = "2017-12-31")

2 Convert prices to returns

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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "GOOG" "IBM"  "QQQ"  "SPY"  "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 GOOG       0.25
## 2 IBM        0.25
## 3 QQQ        0.2 
## 4 SPY        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: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0566 
##  2 2013-02-28  0.00871
##  3 2013-03-28  0.0347 
##  4 2013-04-30  0.0407 
##  5 2013-05-31  0.0927 
##  6 2013-06-28 -0.0168 
##  7 2013-07-31  0.0519 
##  8 2013-08-30 -0.0113 
##  9 2013-09-30  0.0415 
## 10 2013-10-31  0.0322 
## # ℹ 50 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 = "2017-12-31") %>%

    # 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

# 3 Calculating CAPM Beta ----

# A complete list of functions for performance_fun()
# tq_performance_fun_options()

portfolio_market_returns_tbl %>%

    tq_performance(Ra = returns,
                   Rb = market_returns,
                   performance_fun = CAPM.beta)
## # A tibble: 1 × 1
##   CAPM.beta.1
##         <dbl>
## 1       0.958

6 Plot

Scatter with regression line

# Figure 8.2 Scatter with regression line from ggplot ----

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")

Actual versus fitted returns

# Figure 8.5 Actual versus fitted returns ----

portfolio_market_returns_tbl %>%

    # Run regression
    lm(returns ~ market_returns, data = .) %>%

    # Get fitted
    broom::augment() %>%

    # Add date %>%
    mutate(date = portfolio_market_returns_tbl$date) %>%

    # Transform data to long format
    pivot_longer(cols = c(returns, .fitted),
                 names_to = "type",
                 values_to = "returns") %>%

    # Plot
    ggplot(aes(date, returns, color = type)) +
    geom_line()

6 Plot: Scatter with regression line

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 scatter plot shows a clear positive relationship between portfolio returns and market returns, indicating that the portfolio moves in the same direction as the overall market. The slope of the regression line represents the beta coefficient, which measures how sensitive the portfolio is to market movements. Since the points closely follow an upward-sloping trend line, this suggests a relatively strong correlation and supports a beta near or slightly above 1. This means the portfolio tends to rise and fall with the market, confirming that it is moderately to highly sensitive to market performance.