Apply it to your data 3

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.2     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(tidyquant)

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo 
## ── Attaching core tidyquant packages ─────────────────────── tidyquant 1.0.11 ──
## ✔ PerformanceAnalytics 2.0.8      ✔ TTR                  0.24.4
## ✔ quantmod             0.4.28     ✔ xts                  0.14.1── Conflicts ────────────────────────────────────────── tidyquant_conflicts() ──
## ✖ zoo::as.Date()                 masks base::as.Date()
## ✖ zoo::as.Date.numeric()         masks base::as.Date.numeric()
## ✖ dplyr::filter()                masks stats::filter()
## ✖ xts::first()                   masks dplyr::first()
## ✖ dplyr::lag()                   masks stats::lag()
## ✖ xts::last()                    masks dplyr::last()
## ✖ PerformanceAnalytics::legend() masks graphics::legend()
## ✖ quantmod::summary()            masks base::summary()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Get stock prices and convert to returns

Ra <- c("AVGO", "NVDA", "MSFT") %>%
    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 AVGO   2022-01-31 -0.117  
##  2 AVGO   2022-02-28  0.00266
##  3 AVGO   2022-03-31  0.0792 
##  4 AVGO   2022-04-29 -0.120  
##  5 AVGO   2022-05-31  0.0464 
##  6 AVGO   2022-06-30 -0.156  
##  7 AVGO   2022-07-29  0.102  
##  8 AVGO   2022-08-31 -0.0679 
##  9 AVGO   2022-09-30 -0.103  
## 10 AVGO   2022-10-31  0.0588 
## # ℹ 125 more rows

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: 45 × 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 35 more rows

Join the two tables

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

## # A tibble: 135 × 4
## # Groups:   symbol [3]
##    symbol date             Ra      Rb
##    <chr>  <date>        <dbl>   <dbl>
##  1 AVGO   2022-01-31 -0.117   -0.101 
##  2 AVGO   2022-02-28  0.00266 -0.0343
##  3 AVGO   2022-03-31  0.0792   0.0341
##  4 AVGO   2022-04-29 -0.120   -0.133 
##  5 AVGO   2022-05-31  0.0464  -0.0205
##  6 AVGO   2022-06-30 -0.156   -0.0871
##  7 AVGO   2022-07-29  0.102    0.123 
##  8 AVGO   2022-08-31 -0.0679  -0.0464
##  9 AVGO   2022-09-30 -0.103   -0.105 
## 10 AVGO   2022-10-31  0.0588   0.0390
## # ℹ 125 more rows

Calculate CAPM

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

## Registered S3 method overwritten by 'robustbase':
##   method          from     
##   hatvalues.lmrob RobStatTM

RaRb_capm

## # A tibble: 3 × 18
## # Groups:   symbol [3]
##   symbol ActivePremium  Alpha AlphaRobust AnnualizedAlpha  Beta `Beta-`
##   <chr>          <dbl>  <dbl>       <dbl>           <dbl> <dbl>   <dbl>
## 1 AVGO          0.483  0.0331      0.0197          0.478  1.25    1.26 
## 2 NVDA          0.504  0.031       0.0281          0.442  2.12    2.87 
## 3 MSFT          0.0328 0.004       0.0027          0.0492 0.871   0.635
## # ℹ 11 more variables: `Beta-Robust` <dbl>, `Beta+` <dbl>, `Beta+Robust` <dbl>,
## #   BetaRobust <dbl>, Correlation <dbl>, `Correlationp-value` <dbl>,
## #   InformationRatio <dbl>, `R-squared` <dbl>, `R-squaredRobust` <dbl>,
## #   TrackingError <dbl>, TreynorRatio <dbl>

Which stock has a positively skewed distribution of returns?

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

## # A tibble: 3 × 2
## # Groups:   symbol [3]
##   symbol SkewnessKurtosisRatio.1
##   <chr>                    <dbl>
## 1 AVGO                    0.191 
## 2 NVDA                   -0.0464
## 3 MSFT                    0.112