Apply it to your data 3

Which of your stock beat the market in 2022

# Load package
library(tidyverse)
library(tidyquant)

1 Get stock prices and convert to returns

Ra <- c("UA", "NKE", "LULU") %>%
    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: 123 × 3
## # Groups:   symbol [3]
##    symbol date             Ra
##    <chr>  <date>        <dbl>
##  1 UA     2022-01-31 -0.115  
##  2 UA     2022-02-28 -0.0225 
##  3 UA     2022-03-31 -0.00448
##  4 UA     2022-04-29 -0.0880 
##  5 UA     2022-05-31 -0.316  
##  6 UA     2022-06-30 -0.219  
##  7 UA     2022-07-29  0.0897 
##  8 UA     2022-08-31 -0.0811 
##  9 UA     2022-09-30 -0.215  
## 10 UA     2022-10-31  0.101  
## # ℹ 113 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: 41 × 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 31 more rows

3 Join the two tables

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

## # A tibble: 123 × 4
## # Groups:   symbol [3]
##    symbol date             Ra      Rb
##    <chr>  <date>        <dbl>   <dbl>
##  1 UA     2022-01-31 -0.115   -0.101 
##  2 UA     2022-02-28 -0.0225  -0.0343
##  3 UA     2022-03-31 -0.00448  0.0341
##  4 UA     2022-04-29 -0.0880  -0.133 
##  5 UA     2022-05-31 -0.316   -0.0205
##  6 UA     2022-06-30 -0.219   -0.0871
##  7 UA     2022-07-29  0.0897   0.123 
##  8 UA     2022-08-31 -0.0811  -0.0464
##  9 UA     2022-09-30 -0.215   -0.105 
## 10 UA     2022-10-31  0.101    0.0390
## # ℹ 113 more rows

4 Calculate CAPM

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

## # A tibble: 3 × 18
## # Groups:   symbol [3]
##   symbol ActivePremium   Alpha AlphaRobust AnnualizedAlpha  Beta `Beta-`
##   <chr>          <dbl>   <dbl>       <dbl>           <dbl> <dbl>   <dbl>
## 1 UA            -0.318 -0.0243     -0.0277          -0.256 1.29    0.272
## 2 NKE           -0.296 -0.0232     -0.0168          -0.245 0.761   0.692
## 3 LULU          -0.113 -0.0047     -0.0042          -0.055 0.968   0.700
## # 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>