Apply iy to your data 3

# Load Packages
library(tidyverse)
library(tidyquant)

1 Get stock prices and convert to returns

Ra <- c("TSLA", "DKNG", "SMCI") %>%
    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: 87 × 3
## # Groups:   symbol [3]
##    symbol date            Ra
##    <chr>  <date>       <dbl>
##  1 TSLA   2022-01-31 -0.219 
##  2 TSLA   2022-02-28 -0.0708
##  3 TSLA   2022-03-31  0.238 
##  4 TSLA   2022-04-29 -0.192 
##  5 TSLA   2022-05-31 -0.129 
##  6 TSLA   2022-06-30 -0.112 
##  7 TSLA   2022-07-29  0.324 
##  8 TSLA   2022-08-31 -0.0725
##  9 TSLA   2022-09-30 -0.0376
## 10 TSLA   2022-10-31 -0.142 
## # ℹ 77 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: 29 × 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 19 more rows

3 Join the two tables

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

## # A tibble: 87 × 4
## # Groups:   symbol [3]
##    symbol date            Ra      Rb
##    <chr>  <date>       <dbl>   <dbl>
##  1 TSLA   2022-01-31 -0.219  -0.101 
##  2 TSLA   2022-02-28 -0.0708 -0.0343
##  3 TSLA   2022-03-31  0.238   0.0341
##  4 TSLA   2022-04-29 -0.192  -0.133 
##  5 TSLA   2022-05-31 -0.129  -0.0205
##  6 TSLA   2022-06-30 -0.112  -0.0871
##  7 TSLA   2022-07-29  0.324   0.123 
##  8 TSLA   2022-08-31 -0.0725 -0.0464
##  9 TSLA   2022-09-30 -0.0376 -0.105 
## 10 TSLA   2022-10-31 -0.142   0.0390
## # ℹ 77 more rows

4 Calculate CAPM

RaRb_capm <- RaRb %>%
    tq_performance(Ra = Ra, 
                   Rb = Rb, 
                   performance_fun = table.CAPM)
RaRb_capm

## # A tibble: 3 × 13
## # Groups:   symbol [3]
##   symbol ActivePremium   Alpha AnnualizedAlpha  Beta `Beta-` `Beta+` Correlation
##   <chr>          <dbl>   <dbl>           <dbl> <dbl>   <dbl>   <dbl>       <dbl>
## 1 TSLA          -0.310 -0.0185          -0.201  1.80    1.79    4.42       0.658
## 2 DKNG           0.145  0.0189           0.251  1.55    3.00    1.88       0.660
## 3 SMCI           2.38   0.133            3.48   1.72    1.42   -1.60       0.385
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## #   `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>

Which stock has a postively skewed distribution of returns?

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

## # A tibble: 3 × 2
## # Groups:   symbol [3]
##   symbol VolatilitySkewness.1
##   <chr>                 <dbl>
## 1 TSLA                   1.76
## 2 DKNG                   2.86
## 3 SMCI                  21.8