Apply it to your data 3

Which of your stock beat the market in 2022

# Load packages
library(tidyverse)
library(tidyquant)

1 Get stock prices and convert to returns

Ra <- c("KO", "PEP") %>%
    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: 84 × 3
## # Groups:   symbol [2]
##    symbol date              Ra
##    <chr>  <date>         <dbl>
##  1 KO     2022-01-31  0.0288  
##  2 KO     2022-02-28  0.0202  
##  3 KO     2022-03-31  0.00377 
##  4 KO     2022-04-29  0.0421  
##  5 KO     2022-05-31 -0.0190  
##  6 KO     2022-06-30 -0.000244
##  7 KO     2022-07-29  0.0200  
##  8 KO     2022-08-31 -0.0383  
##  9 KO     2022-09-30 -0.0856  
## 10 KO     2022-10-31  0.0684  
## # ℹ 74 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: 42 × 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 32 more rows

3 Join the two tables

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

## # A tibble: 84 × 4
## # Groups:   symbol [2]
##    symbol date              Ra      Rb
##    <chr>  <date>         <dbl>   <dbl>
##  1 KO     2022-01-31  0.0288   -0.101 
##  2 KO     2022-02-28  0.0202   -0.0343
##  3 KO     2022-03-31  0.00377   0.0341
##  4 KO     2022-04-29  0.0421   -0.133 
##  5 KO     2022-05-31 -0.0190   -0.0205
##  6 KO     2022-06-30 -0.000244 -0.0871
##  7 KO     2022-07-29  0.0200    0.123 
##  8 KO     2022-08-31 -0.0383   -0.0464
##  9 KO     2022-09-30 -0.0856   -0.105 
## 10 KO     2022-10-31  0.0684    0.0390
## # ℹ 74 more rows

4 Calculate CAPM

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

## # A tibble: 2 × 18
## # Groups:   symbol [2]
##   symbol ActivePremium   Alpha AlphaRobust AnnualizedAlpha   Beta `Beta-`
##   <chr>          <dbl>   <dbl>       <dbl>           <dbl>  <dbl>   <dbl>
## 1 KO            0.0171  0.0067      0.0047          0.0838 0.0725 -0.142 
## 2 PEP          -0.113  -0.0044     -0.0061         -0.0515 0.111  -0.0373
## # 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 = table.CAPM)
RaRb_capm

## # A tibble: 2 × 18
## # Groups:   symbol [2]
##   symbol ActivePremium   Alpha AlphaRobust AnnualizedAlpha   Beta `Beta-`
##   <chr>          <dbl>   <dbl>       <dbl>           <dbl>  <dbl>   <dbl>
## 1 KO            0.0171  0.0067      0.0047          0.0838 0.0725 -0.142 
## 2 PEP          -0.113  -0.0044     -0.0061         -0.0515 0.111  -0.0373
## # 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>