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("AAPL", "GOOG", "NFLX") %>%
    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: 138 × 3
## # Groups:   symbol [3]
##    symbol date            Ra
##    <chr>  <date>       <dbl>
##  1 AAPL   2022-01-31 -0.0397
##  2 AAPL   2022-02-28 -0.0541
##  3 AAPL   2022-03-31  0.0575
##  4 AAPL   2022-04-29 -0.0971
##  5 AAPL   2022-05-31 -0.0545
##  6 AAPL   2022-06-30 -0.0814
##  7 AAPL   2022-07-29  0.189 
##  8 AAPL   2022-08-31 -0.0312
##  9 AAPL   2022-09-30 -0.121 
## 10 AAPL   2022-10-31  0.110 
## # ℹ 128 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: 46 × 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 36 more rows

3 Join the two tables

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

## # A tibble: 138 × 4
## # Groups:   symbol [3]
##    symbol date            Ra      Rb
##    <chr>  <date>       <dbl>   <dbl>
##  1 AAPL   2022-01-31 -0.0397 -0.101 
##  2 AAPL   2022-02-28 -0.0541 -0.0343
##  3 AAPL   2022-03-31  0.0575  0.0341
##  4 AAPL   2022-04-29 -0.0971 -0.133 
##  5 AAPL   2022-05-31 -0.0545 -0.0205
##  6 AAPL   2022-06-30 -0.0814 -0.0871
##  7 AAPL   2022-07-29  0.189   0.123 
##  8 AAPL   2022-08-31 -0.0312 -0.0464
##  9 AAPL   2022-09-30 -0.121  -0.105 
## 10 AAPL   2022-10-31  0.110   0.0390
## # ℹ 128 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 AAPL         -0.0003 0.0018      0.0028          0.022  0.905   0.967
## 2 GOOG          0.0697 0.007       0.007           0.0867 0.974   0.997
## 3 NFLX          0.0586 0.0073      0.0183          0.0907 1.53    2.24 
## # 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: 3 × 18
## # Groups:   symbol [3]
##   symbol ActivePremium  Alpha AlphaRobust AnnualizedAlpha  Beta `Beta-`
##   <chr>          <dbl>  <dbl>       <dbl>           <dbl> <dbl>   <dbl>
## 1 AAPL         -0.0003 0.0018      0.0028          0.022  0.905   0.967
## 2 GOOG          0.0697 0.007       0.007           0.0867 0.974   0.997
## 3 NFLX          0.0586 0.0073      0.0183          0.0907 1.53    2.24 
## # 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>