Apply 3

# Load packages
library(tidyverse)
library(tidyquant)

1 Get stock prices and convert to returns

stocks <- tq_get(c("VOOG", "NVDA"),
                 get = "stock.prices",
                 from = "2019-01-01",
                 to = "2022-01-01")
stocks

## # A tibble: 1,514 × 8
##    symbol date        open  high   low close volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
##  1 VOOG   2019-01-02  133.  135.  133.  135. 239200     129.
##  2 VOOG   2019-01-03  134.  134.  131.  131. 177400     126.
##  3 VOOG   2019-01-04  133.  137.  133.  136. 198400     130.
##  4 VOOG   2019-01-07  136.  138.  136.  137. 262000     131.
##  5 VOOG   2019-01-08  139.  139.  137.  139. 178600     133.
##  6 VOOG   2019-01-09  139.  140.  138.  139. 207000     133.
##  7 VOOG   2019-01-10  139.  140.  138.  140. 156200     134.
##  8 VOOG   2019-01-11  139.  140.  139.  140. 123400     134.
##  9 VOOG   2019-01-14  139.  139.  138.  139.  75500     133.
## 10 VOOG   2019-01-15  139.  141.  139.  141. 128500     135.
## # ℹ 1,504 more rows

Ra <- c("VOOG", "NVDA") %>%
    tq_get(get  = "stock.prices",
           from = "2022-01-01") %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 col_rename = "Ra")
Ra

## # A tibble: 42 × 3
## # Groups:   symbol [2]
##    symbol date            Ra
##    <chr>  <date>       <dbl>
##  1 VOOG   2022-01-31 -0.0909
##  2 VOOG   2022-02-28 -0.0445
##  3 VOOG   2022-03-31  0.0455
##  4 VOOG   2022-04-29 -0.126 
##  5 VOOG   2022-05-31 -0.0134
##  6 VOOG   2022-06-30 -0.0823
##  7 VOOG   2022-07-29  0.128 
##  8 VOOG   2022-08-31 -0.0542
##  9 VOOG   2022-09-30 -0.0997
## 10 VOOG   2022-10-31  0.0455
## # ℹ 32 more rows

2 Get baseline and convert to returns

Rb <- "^IXIC" %>%
    tq_get(get  = "stock.prices",
           from = "2022-01-01") %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 col_rename = "Rb")
Rb

## # A tibble: 21 × 2
##    date            Rb
##    <date>       <dbl>
##  1 2022-01-31 -0.101 
##  2 2022-02-28 -0.0343
##  3 2022-03-31  0.0341
##  4 2022-04-29 -0.133 
##  5 2022-05-31 -0.0205
##  6 2022-06-30 -0.0871
##  7 2022-07-29  0.123 
##  8 2022-08-31 -0.0464
##  9 2022-09-30 -0.105 
## 10 2022-10-31  0.0390
## # ℹ 11 more rows

3 Join the two tables

RaRb <- left_join(Ra, Rb, by = c("date" = "date"))
RaRb

## # A tibble: 42 × 4
## # Groups:   symbol [2]
##    symbol date            Ra      Rb
##    <chr>  <date>       <dbl>   <dbl>
##  1 VOOG   2022-01-31 -0.0909 -0.101 
##  2 VOOG   2022-02-28 -0.0445 -0.0343
##  3 VOOG   2022-03-31  0.0455  0.0341
##  4 VOOG   2022-04-29 -0.126  -0.133 
##  5 VOOG   2022-05-31 -0.0134 -0.0205
##  6 VOOG   2022-06-30 -0.0823 -0.0871
##  7 VOOG   2022-07-29  0.128   0.123 
##  8 VOOG   2022-08-31 -0.0542 -0.0464
##  9 VOOG   2022-09-30 -0.0997 -0.105 
## 10 VOOG   2022-10-31  0.0455  0.0390
## # ℹ 32 more rows

4 Calculate CAPM

RaRb_capm <- RaRb %>%
    tq_performance(Ra = Ra, 
                   Rb = NULL, 
                   performance_fun = skewness)
RaRb_capm

## # A tibble: 2 × 2
## # Groups:   symbol [2]
##   symbol skewness.1
##   <chr>       <dbl>
## 1 VOOG      -0.0266
## 2 NVDA      -0.0591