# Load Packages
library(tidyverse)
library(tidyquant)

1 webGet stock prices and convert to returns

Ra <- c("MSFT", "NKE", "WMT") %>%
    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: 135 × 3
## # Groups:   symbol [3]
##    symbol date             Ra
##    <chr>  <date>        <dbl>
##  1 MSFT   2022-01-31 -0.0710 
##  2 MSFT   2022-02-28 -0.0372 
##  3 MSFT   2022-03-31  0.0319 
##  4 MSFT   2022-04-29 -0.0999 
##  5 MSFT   2022-05-31 -0.0181 
##  6 MSFT   2022-06-30 -0.0553 
##  7 MSFT   2022-07-29  0.0931 
##  8 MSFT   2022-08-31 -0.0667 
##  9 MSFT   2022-09-30 -0.109  
## 10 MSFT   2022-10-31 -0.00331
## # ℹ 125 more rows

2 Get baseline and convert to returns

Rb <- "^IXIC" %>%
    tq_get(get = "stock.prices",
           from = "2022-01-01") %>%
    group_by(symbol) %>%
    tq_transmute(select = adjusted,
                 mutate_fun = periodReturn,
                 period = "monthly",
                 col_rename = "Rb")
Rb
## # A tibble: 45 × 3
## # Groups:   symbol [1]
##    symbol date            Rb
##    <chr>  <date>       <dbl>
##  1 ^IXIC  2022-01-31 -0.101 
##  2 ^IXIC  2022-02-28 -0.0343
##  3 ^IXIC  2022-03-31  0.0341
##  4 ^IXIC  2022-04-29 -0.133 
##  5 ^IXIC  2022-05-31 -0.0205
##  6 ^IXIC  2022-06-30 -0.0871
##  7 ^IXIC  2022-07-29  0.123 
##  8 ^IXIC  2022-08-31 -0.0464
##  9 ^IXIC  2022-09-30 -0.105 
## 10 ^IXIC  2022-10-31  0.0390
## # ℹ 35 more rows

3 oin the two tables

RaRb <- left_join(Ra, Rb, by = c("date" = "date"))
RaRb
## # A tibble: 135 × 5
##    symbol.x date             Ra symbol.y      Rb
##    <chr>    <date>        <dbl> <chr>      <dbl>
##  1 MSFT     2022-01-31 -0.0710  ^IXIC    -0.101 
##  2 MSFT     2022-02-28 -0.0372  ^IXIC    -0.0343
##  3 MSFT     2022-03-31  0.0319  ^IXIC     0.0341
##  4 MSFT     2022-04-29 -0.0999  ^IXIC    -0.133 
##  5 MSFT     2022-05-31 -0.0181  ^IXIC    -0.0205
##  6 MSFT     2022-06-30 -0.0553  ^IXIC    -0.0871
##  7 MSFT     2022-07-29  0.0931  ^IXIC     0.123 
##  8 MSFT     2022-08-31 -0.0667  ^IXIC    -0.0464
##  9 MSFT     2022-09-30 -0.109   ^IXIC    -0.105 
## 10 MSFT     2022-10-31 -0.00331 ^IXIC     0.0390
## # ℹ 125 more rows

4 Calculate CAPM

RaRb_skewness <- RaRb %>%
    tq_performance(Ra = Ra, 
                   Rb = NULL, 
                   performance_fun = skewness)
RaRb_skewness
## # A tibble: 1 × 1
##   skewness.1
##        <dbl>
## 1     -0.344

Which stock is postively skewed?

None of them are positively skewed