# 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

symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG")
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
symbol <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "SPY" "EFA" "IJS" "EEM" "AGG"
# 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 SPY        0.25
## 2 EFA        0.25
## 3 IJS        0.2 
## 4 EEM        0.2 
## 5 AGG        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  0.0308  
##  2 2013-02-28 -0.000870
##  3 2013-03-28  0.0187  
##  4 2013-04-30  0.0206  
##  5 2013-05-31 -0.00535 
##  6 2013-06-28 -0.0229  
##  7 2013-07-31  0.0412  
##  8 2013-08-30 -0.0255  
##  9 2013-09-30  0.0544  
## 10 2013-10-31  0.0352  
## # ℹ 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

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       0.891

6 Plot

Scatterplot of Returns with Regression Line

portfolio_market_returns_tbl %>%
    
    ggplot(aes(x = market_returns,
               y = portfolio_returns)) +
    geom_point(color = "violet") +
    geom_smooth(method = "lm", 
                se = FALSE, 
                size = 1.5,
                color = tidyquant::palette_light()[3]) +
    
    labs(y = "Portfolio Returns",
         x = "Market Returns")

Line Plot of Actual and Fitted 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()