# 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("NDAQ", "SNEX", "ICE", "CME")
prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31")

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] "CME"  "ICE"  "NDAQ" "SNEX"
# weights
weights <- c(0.25, 0.25, 0.25, 0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 CME        0.25
## 2 ICE        0.25
## 3 NDAQ       0.25
## 4 SNEX       0.25

4 Build a 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: 138 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0944
##  2 2013-02-28  0.0676
##  3 2013-03-28  0.0219
##  4 2013-04-30 -0.0295
##  5 2013-05-31  0.0652
##  6 2013-06-28  0.0460
##  7 2013-07-31  0.0120
##  8 2013-08-30 -0.0249
##  9 2013-09-30  0.0481
## 10 2013-10-31  0.0410
## # ℹ 128 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x = "NDAQ",
       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") %>%
    
    slice(-1)

5.2 Join returns

portfolio_market_tbl <- left_join(market_returns_tbl, portfolio_returns_tbl, by = "date") %>%
    
    set_names("date", "market_returns", "portfolio_returns")

5.3 CAPM Beta

portfolio_market_tbl %>%
    
    tq_performance(Ra = portfolio_returns,
                   Rb = market_returns, 
                   performance_fun = CAPM.beta)
## # A tibble: 1 × 1
##   CAPM.beta.1
##         <dbl>
## 1       0.647

6 Plot: Scatter with regression line

portfolio_market_tbl %>%
    
    ggplot(aes(x = market_returns,
               y = portfolio_returns)) +
    geom_point() +
    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?

The portfolio is right in line with the market having a similar look as the Code Along example, however the plots on my regression line are spread further apart. my beta of 0.647 is lower than the market value meaning that it is less risky