# 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", "GOOGL", "ORCL", "JNJ")
prices <- tq_get(x = symbols, 
                 from = "2012-12-31", 
                 to = "2017-12-31")

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] "GOOGL" "JNJ"   "NVDA"  "ORCL"  "TSLA"
# weights
weight <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 GOOGL     0.25
## 2 JNJ       0.25
## 3 NVDA      0.2 
## 4 ORCL      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  0.0527 
##  2 2013-02-28  0.0169 
##  3 2013-03-28  0.0146 
##  4 2013-04-30  0.0728 
##  5 2013-05-31  0.0888 
##  6 2013-06-28 -0.00817
##  7 2013-07-31  0.0626 
##  8 2013-08-30 -0.00442
##  9 2013-09-30  0.0415 
## 10 2013-10-31  0.0361 
## # ℹ 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x = "SPY", 
       ge = "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

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       0.932

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") +
    
    # To set the limits (zoom window) for both the X and Y axes, 
    # forcing the plot to display only the range from 0 to 0.1 (or 0% to 10%) on both axes
    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? In terms of how sensitive my portfolio is to the market, it’s very close to a beta of 1, with 0.932, making it slightly less volatile than the baseline market returns. The plot confirms this, as some of the points are above the line, with some below, and quite a few right on the line.