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("PLUG", "GOOG", "NVDA") %>%
    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: 99 × 3
## # Groups:   symbol [3]
##    symbol date           Ra
##    <chr>  <date>      <dbl>
##  1 PLUG   2022-01-31 -0.240
##  2 PLUG   2022-02-28  0.156
##  3 PLUG   2022-03-31  0.131
##  4 PLUG   2022-04-29 -0.265
##  5 PLUG   2022-05-31 -0.121
##  6 PLUG   2022-06-30 -0.103
##  7 PLUG   2022-07-29  0.288
##  8 PLUG   2022-08-31  0.314
##  9 PLUG   2022-09-30 -0.251
## 10 PLUG   2022-10-31 -0.239
## # ℹ 89 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: 33 × 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 23 more rows

3 Join the two tables

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

## # A tibble: 99 × 4
## # Groups:   symbol [3]
##    symbol date           Ra      Rb
##    <chr>  <date>      <dbl>   <dbl>
##  1 PLUG   2022-01-31 -0.240 -0.101 
##  2 PLUG   2022-02-28  0.156 -0.0343
##  3 PLUG   2022-03-31  0.131  0.0341
##  4 PLUG   2022-04-29 -0.265 -0.133 
##  5 PLUG   2022-05-31 -0.121 -0.0205
##  6 PLUG   2022-06-30 -0.103 -0.0871
##  7 PLUG   2022-07-29  0.288  0.123 
##  8 PLUG   2022-08-31  0.314 -0.0464
##  9 PLUG   2022-09-30 -0.251 -0.105 
## 10 PLUG   2022-10-31 -0.239  0.0390
## # ℹ 89 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 PLUG         -0.665  -0.0603         -0.526  1.43    1.50    2.14        0.409
## 2 GOOG         -0.0036  0.0012          0.0146 0.914   0.982   0.681       0.754
## 3 NVDA          0.609   0.0431          0.660  2.27    2.77    1.54        0.869
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## #   `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>

Which stock has a positively skewed distrubution of returns?

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 PLUG         -0.665  -0.0603         -0.526  1.43    1.50    2.14        0.409
## 2 GOOG         -0.0036  0.0012          0.0146 0.914   0.982   0.681       0.754
## 3 NVDA          0.609   0.0431          0.660  2.27    2.77    1.54        0.869
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## #   `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>