# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Collect individual returns into a portfolio by assigning a weight to each stock

five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”

from 2012-12-31 to 2017-12-31

1 Import stock prices

# Choose stocks

symbols <- c("SPY", "NVDA", "VOOG")


prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns

 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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()

weights <- c (.25, .50, .25)
weights
## [1] 0.25 0.50 0.25
weights_tbl <- tibble(symbols, weights)
weights_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 NVDA       0.25
## 2 SPY        0.5 
## 3 VOOG       0.25

4 Build a portfolio

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

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0347
##  2 2013-02-28  0.0193
##  3 2013-03-28  0.0310
##  4 2013-04-30  0.0322
##  5 2013-05-31  0.0311
##  6 2013-06-28 -0.0173
##  7 2013-07-31  0.0436
##  8 2013-08-30 -0.0150
##  9 2013-09-30  0.0384
## 10 2013-10-31  0.0286
## # ℹ 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

marktet_returns_tbl <- tq_get(x = "SPY", 
               get = "stock.prices",
              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 <- left_join(marktet_returns_tbl,
                                  portfolio_returns_tbl, 
                                  "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        1.03

6 Plot

Scatterplot of returns 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")

Line Plot of fitted vs actual returns

actual_fitted_long_tbl <- portfolio_market_returns_tbl %>%
    
    #Linear regression model
    lm(portfolio_returns ~ market_returns, data = .) %>%
    
    # Get fitted and actual returns
    broom::augment() %>%
    
    # Add date
    mutate(date = portfolio_market_returns_tbl$date) %>%
    select(date, portfolio_returns, .fitted) %>%
    
    #Transform data to long 
    pivot_longer(cols = c(portfolio_returns, .fitted),
                 names_to = "type", 
                 values_to = "returns")

actual_fitted_long_tbl %>%
    ggplot(aes(x = date, y = returns, color = type)) +
    geom_line()

How sensitive is your portfolio to the market? Discuss in terms of the beta coefficient. Does the plot confirm the beta coefficient you calculated? Considering I am dealing with etfs and companys dealing with the fortune 500 mine is a direct indicator and can be greatky impacted by the market.