# 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", "DELL")

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 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] "DELL" "TSLA"
# weights
weights <- c(0.50,0.50)
weights
## [1] 0.5 0.5
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 2 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 DELL        0.5
## 2 TSLA        0.5

4 Build a portfolio

#?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col   = asset,
                 returns_col  = returns,
                 weights      = w_tbl,
                 col_rename   = "returns",
                 rebalance_on = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0510
##  2 2013-02-28 -0.0371
##  3 2013-03-28  0.0421
##  4 2013-04-30  0.177 
##  5 2013-05-31  0.297 
##  6 2013-06-28  0.0468
##  7 2013-07-31  0.112 
##  8 2013-08-30  0.115 
##  9 2013-09-30  0.0674
## 10 2013-10-31 -0.0949
## # ℹ 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x      = "TSLA",
                 get    = "stock.prices",
                 from   = "2012-12-31",
                 to     = "2017-12-31") %>%
    # Convert prices to monthly 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       0.503

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")

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 has a very strong linear relationship and a 45 degree regression line meaning they are very sensitive to the market.This confirms my calculation.