# 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", "GM", "F", "VWAGY", "HMC")
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] "F"     "GM"    "HMC"   "TSLA"  "VWAGY"
# weights
weight <- c(0.15, 0.25, 0.2, 0.3, 0.1)
weight
## [1] 0.15 0.25 0.20 0.30 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 F         0.15
## 2 GM        0.25
## 3 HMC       0.2 
## 4 TSLA      0.3 
## 5 VWAGY     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  0.0357
##  2 2013-02-28 -0.0476
##  3 2013-03-28  0.0340
##  4 2013-04-30  0.150 
##  5 2013-05-31  0.220 
##  6 2013-06-28  0.0120
##  7 2013-07-31  0.115 
##  8 2013-08-30  0.0404
##  9 2013-09-30  0.0750
## 10 2013-10-31 -0.0302
## # … with 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x  =  "DJI", 
                  get  = "stock.prices",
                  from = "2012-12-31",
                  to   = "2017-12-31")%>%
    
    # Converrt 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        1.02

6 Plot: Scatter with regression line

portfolio_market_returns_tbl %>%
        
    ggplot(aes(x = market_returns,
               y = portfolio_returns))+
    geom_point() +
    geom_smooth(method = "lm", se = FALSE, 
                linewidth = 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 beta coefficient fo 1.02. A positive coefficient greater than one means it is more volatile than the market but only by .02, so the portfolio is closer to 1, to be as volatile as the market. Meaning that it moves with the market. But there is not a strong linear relationship, the dots are not near the regression line, but rather, all over the place surrounding it.