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

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] "AAPL" "AMZN" "META" "MSFT" "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 META       0.2 
## 4 MSFT       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 = "Portfolio Returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       `Portfolio Returns`
##    <date>                   <dbl>
##  1 2013-01-31             0.0213 
##  2 2013-02-28            -0.0365 
##  3 2013-03-28             0.00432
##  4 2013-04-30             0.0689 
##  5 2013-05-31             0.0653 
##  6 2013-06-28            -0.0121 
##  7 2013-07-31             0.138  
##  8 2013-08-30             0.0597 
##  9 2013-09-30             0.0732 
## 10 2013-10-31             0.0544 
## # … with 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

markets_returns_tbl <- tq_get(x    = "QQQ",
                 get  = "stock.prices",    
                 from = "2012-12-31",
                 to   = "2017-12-31")  %>%
    
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly",
                 type       = "log", 
                 col_rename = "Market Return") %>%
    
    slice(-1)

markets_returns_tbl
## # A tibble: 60 × 2
##    date       `Market Return`
##    <date>               <dbl>
##  1 2013-01-31         0.0264 
##  2 2013-02-28         0.00343
##  3 2013-03-28         0.0298 
##  4 2013-04-30         0.0251 
##  5 2013-05-31         0.0351 
##  6 2013-06-28        -0.0242 
##  7 2013-07-31         0.0612 
##  8 2013-08-30        -0.00397
##  9 2013-09-30         0.0472 
## 10 2013-10-31         0.0484 
## # … with 50 more rows

5.2 Join returns

portfolio_market_returns_tbl <- left_join(markets_returns_tbl, portfolio_returns_tbl, "date") %>%
    
    set_names("date", "market_returns", "portfolio_returns")

portfolio_market_returns_tbl
## # A tibble: 60 × 3
##    date       market_returns portfolio_returns
##    <date>              <dbl>             <dbl>
##  1 2013-01-31        0.0264            0.0213 
##  2 2013-02-28        0.00343          -0.0365 
##  3 2013-03-28        0.0298            0.00432
##  4 2013-04-30        0.0251            0.0689 
##  5 2013-05-31        0.0351            0.0653 
##  6 2013-06-28       -0.0242           -0.0121 
##  7 2013-07-31        0.0612            0.138  
##  8 2013-08-30       -0.00397           0.0597 
##  9 2013-09-30        0.0472            0.0732 
## 10 2013-10-31        0.0484            0.0544 
## # … with 50 more rows

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        1.15

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?

My portfolio is more sensitive than the market and will move more than the market on average both to the upside and downside. My beta coefficient is 1.15 meaning my portfolio should move 15% more than the makret. The plot does confirm it and it is linear however not as linear as the portfolio from the Code Along assingment with the returns being further from the 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")