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("LULU", "AMZN", "TSLA") %>%
    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: 27 × 3
## # Groups:   symbol [3]
##    symbol date            Ra
##    <chr>  <date>       <dbl>
##  1 LULU   2022-01-31 -0.139 
##  2 LULU   2022-02-28 -0.0414
##  3 LULU   2022-03-31  0.142 
##  4 LULU   2022-04-29 -0.0290
##  5 LULU   2022-05-31 -0.175 
##  6 LULU   2022-06-30 -0.0686
##  7 LULU   2022-07-29  0.139 
##  8 LULU   2022-08-31 -0.0340
##  9 LULU   2022-09-21  0.0605
## 10 AMZN   2022-01-31 -0.122 
## # … with 17 more rows

2 Get baseline and convert to returns

Rb <- "XLK" %>%
    tq_get(get  = "stock.prices",
           from = "2022-01-01") %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 col_rename = "Rb")
Rb

## # A tibble: 9 × 2
##   date             Rb
##   <date>        <dbl>
## 1 2022-01-31 -0.0772 
## 2 2022-02-28 -0.0488 
## 3 2022-03-31  0.0335 
## 4 2022-04-29 -0.110  
## 5 2022-05-31 -0.00686
## 6 2022-06-30 -0.0926 
## 7 2022-07-29  0.135  
## 8 2022-08-31 -0.0621 
## 9 2022-09-21 -0.0612

3 Join the two tables

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

## # A tibble: 27 × 4
## # Groups:   symbol [3]
##    symbol date            Ra       Rb
##    <chr>  <date>       <dbl>    <dbl>
##  1 LULU   2022-01-31 -0.139  -0.0772 
##  2 LULU   2022-02-28 -0.0414 -0.0488 
##  3 LULU   2022-03-31  0.142   0.0335 
##  4 LULU   2022-04-29 -0.0290 -0.110  
##  5 LULU   2022-05-31 -0.175  -0.00686
##  6 LULU   2022-06-30 -0.0686 -0.0926 
##  7 LULU   2022-07-29  0.139   0.135  
##  8 LULU   2022-08-31 -0.0340 -0.0621 
##  9 LULU   2022-09-21  0.0605 -0.0612 
## 10 AMZN   2022-01-31 -0.122  -0.0772 
## # … with 17 more rows

4 Caculate CAPM

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

## # A tibble: 3 × 13
## # Groups:   symbol [3]
##   symbol ActivePr…¹  Alpha Annua…²  Beta `Beta-` `Beta+` Corre…³ Corre…⁴ Infor…⁵
##   <chr>       <dbl>  <dbl>   <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1 LULU       0.115  0.0121   0.155 0.874  -0.913  -0.025   0.599  0.0882  0.372 
## 2 AMZN      -0.0369 0.0268   0.373 1.78    2.00    2.07    0.948  0.0001 -0.143 
## 3 TSLA       0.0307 0.0527   0.852 2.12    0.834   0.848   0.846  0.004   0.0667
## # … with 3 more variables: `R-squared` <dbl>, TrackingError <dbl>,
## #   TreynorRatio <dbl>, and abbreviated variable names ¹​ActivePremium,
## #   ²​AnnualizedAlpha, ³​Correlation, ⁴​`Correlationp-value`, ⁵​InformationRatio

Which stock has a positively skewed distribution of returns?

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

## # A tibble: 3 × 2
## # Groups:   symbol [3]
##   symbol VolatilitySkewness.1
##   <chr>                 <dbl>
## 1 LULU                   3.73
## 2 AMZN                   1.80
## 3 TSLA                   2.90