# 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", "AAPL", "NFLX")
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] "AAPL" "NFLX" "TSLA"
# weights
weights <- c(.25, .25, .2)
weights
## [1] 0.25 0.25 0.20
w_tble <- tibble(symbols, weights)
w_tble
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 NFLX       0.25
## 3 TSLA       0.2

4 Build a portfolio

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

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        Returns
##    <date>        <dbl>
##  1 2013-01-31  0.126  
##  2 2013-02-28  0.0111 
##  3 2013-03-28  0.0191 
##  4 2013-04-30  0.104  
##  5 2013-05-31  0.136  
##  6 2013-06-28 -0.0301 
##  7 2013-07-31  0.114  
##  8 2013-08-30  0.103  
##  9 2013-09-30  0.0429 
## 10 2013-10-31 -0.00445
## # … with 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x    = "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

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

5.3 CAPM Beta

portfoli_market_returns_tbl %>%
    
    tq_performance(Ra = portfolio_returns,
                   Rb = market_returns,
                   performance_fun = CAPM.beta)
## # A tibble: 1 × 1
##   CAPM.beta.1
##         <dbl>
## 1       0.775

6 Plot: Scatter with regression line

portfoli_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 = "Porfolio 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?

There isn’t a very strong relation between my portfolio and the market. The dot’s stray from the line so there isnt a strong linear relationship.