# 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("XOM", "QQQ", "SPY", "TSLA","CGC")

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] "CGC"  "QQQ"  "SPY"  "TSLA" "XOM"
# weights
weights <- c(0.25,0.25,0.2,0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 CGC        0.25
## 2 QQQ        0.25
## 3 SPY        0.2 
## 4 TSLA       0.2 
## 5 XOM        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  0.0409 
##  2 2013-02-28 -0.0113 
##  3 2013-03-28  0.0324 
##  4 2013-04-30  0.0796 
##  5 2013-05-31  0.135  
##  6 2013-06-28  0.00985
##  7 2013-07-31  0.0738 
##  8 2013-08-30  0.0323 
##  9 2013-09-30  0.0437 
## 10 2013-10-31 -0.0127 
## # ℹ 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") %>%
    
    # 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 <- portfolio_returns_tbl %>%

    mutate(market_returns = market_returns_tbl %>% pull(returns))

5.3 CAPM Beta

portfolio_market_returns_tbl %>%

    tq_performance(Ra = returns,
                   Rb = market_returns,
                   performance_fun = CAPM.beta)
## # A tibble: 1 × 1
##   CAPM.beta.1
##         <dbl>
## 1        1.23

6 Plot: Scatter with regression line

# Scatter with Regression Line

portfolio_market_returns_tbl %>%

    ggplot(aes(market_returns, returns)) +
    geom_point(color = "cornflowerblue") +

    geom_smooth(method = "lm", se = FALSE,
                size = 1.5, color = tidyquant::palette_light()[3]) +

    labs(x = "market returns",
         y = "portfolio 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 is relatively sensitive to the market with a beta coefficient of 1.23. Which means I have positive portfolio return as the beta is a positive number and the line is slanted upward.

Though there is some volatility with a variation as I have data points which range from down below portfolio returns -0.10 and market returns -0.04 and my highest being at 0.15 Portfolio returns and 0.08 Market returns. With most of my data being between portfolio returns of -0.05 to positive 0.075 and having market returns between -0.02 and 0.06.

In other words what my portfolio beta of 1.23 means that for every 10% the market grows by, my portfolio grows by 23%.