# 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("SPY", "HMC", "WMT", "TGT")

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] "HMC" "SPY" "TGT" "WMT"
# weights 
weight <- c(0.2, 0.2, 0.2, 0.4)
weight
## [1] 0.2 0.2 0.2 0.4
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 4 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 HMC        0.2
## 2 SPY        0.2
## 3 TGT        0.2
## 4 WMT        0.4

4 Build a portfolio

# ?tq_portfolio

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

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0281 
##  2 2013-02-28  0.0153 
##  3 2013-03-28  0.0545 
##  4 2013-04-30  0.0337 
##  5 2013-05-31 -0.0221 
##  6 2013-06-28 -0.00719
##  7 2013-07-31  0.0351 
##  8 2013-08-30 -0.0599 
##  9 2013-09-30  0.0258 
## 10 2013-10-31  0.0358 
## # … with 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x    = "SPY", 
                 from = "2012-12-31",
                 to   = "2017-12-31") %>%
    
    # Covert prices to return 
    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       0.701

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 CAMP beta of 0.7, which means in terms of beta coefficient my portfolio is less volatile compared to the market. My chart has a upward slope, so it confirms beta coefficient and the data points are mostly clumped towards the regression line. When my data points are clumped toward my regression line it means that the points are strong, meaning they are more volatile. They have a positive look towards the market with a upward slope but not a great volatile because the beta coefficient isn’t above 1.