# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

1 Import stock prices

symbols <- c("VOO", "IVV", "^IXIC", "VTSAX", "FBGRX")
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] "FBGRX" "IVV"   "VOO"   "VTSAX" "^IXIC"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 FBGRX       0.2
## 2 IVV         0.2
## 3 VOO         0.2
## 4 VTSAX       0.2
## 5 ^IXIC       0.2

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  0.0474
##  2 2013-02-28  0.0115
##  3 2013-03-28  0.0349
##  4 2013-04-30  0.0180
##  5 2013-05-31  0.0305
##  6 2013-06-28 -0.0157
##  7 2013-07-31  0.0579
##  8 2013-08-30 -0.0224
##  9 2013-09-30  0.0395
## 10 2013-10-31  0.0422
## # ℹ 50 more rows

5 Calculate CAPM Beta

5.1 Get Market returns

Market_returns_tbl <- tq_get(x = "^IXIC",
                 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")

Portfolio_market_returns_tbl
## # A tibble: 60 × 3
##    date       market_returns portfolio_returns
##    <date>              <dbl>             <dbl>
##  1 2013-01-31        0.0398             0.0474
##  2 2013-02-28        0.00573            0.0115
##  3 2013-03-28        0.0334             0.0349
##  4 2013-04-30        0.0186             0.0180
##  5 2013-05-31        0.0375             0.0305
##  6 2013-06-28       -0.0154            -0.0157
##  7 2013-07-31        0.0635             0.0579
##  8 2013-08-30       -0.0101            -0.0224
##  9 2013-09-30        0.0494             0.0395
## 10 2013-10-31        0.0386             0.0422
## # ℹ 50 more rows

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       0.856

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? Discuss in terms of the beta coefficient. Does the plot confirm the beta coefficient you calculated.

My Portfolio is not that sensitive the CAPM beta being under 1 indicates my portfolio is less volitile than the market.