Apply it to your data 3

Which one of your stock beat the market in 2022

# Load packages
library(tidyverse)
library(tidyquant)

Get stock prices and convert to returns

Ra <- c("XOM", "NVDA", "SNAP","TSLA","WIX") %>%
    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: 165 × 3
## # Groups:   symbol [5]
##    symbol date             Ra
##    <chr>  <date>        <dbl>
##  1 XOM    2022-01-31  0.195  
##  2 XOM    2022-02-28  0.0438 
##  3 XOM    2022-03-31  0.0532 
##  4 XOM    2022-04-29  0.0322 
##  5 XOM    2022-05-31  0.138  
##  6 XOM    2022-06-30 -0.108  
##  7 XOM    2022-07-29  0.132  
##  8 XOM    2022-08-31 -0.00424
##  9 XOM    2022-09-30 -0.0866 
## 10 XOM    2022-10-31  0.269  
## # ℹ 155 more rows

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

Join the two data tables

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

## # A tibble: 165 × 4
## # Groups:   symbol [5]
##    symbol date             Ra      Rb
##    <chr>  <date>        <dbl>   <dbl>
##  1 XOM    2022-01-31  0.195   -0.101 
##  2 XOM    2022-02-28  0.0438  -0.0343
##  3 XOM    2022-03-31  0.0532   0.0341
##  4 XOM    2022-04-29  0.0322  -0.133 
##  5 XOM    2022-05-31  0.138   -0.0205
##  6 XOM    2022-06-30 -0.108   -0.0871
##  7 XOM    2022-07-29  0.132    0.123 
##  8 XOM    2022-08-31 -0.00424 -0.0464
##  9 XOM    2022-09-30 -0.0866  -0.105 
## 10 XOM    2022-10-31  0.269    0.0390
## # ℹ 155 more rows

Calculate CAPM

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

## # A tibble: 5 × 13
## # Groups:   symbol [5]
##   symbol ActivePremium   Alpha AnnualizedAlpha  Beta `Beta-` `Beta+` Correlation
##   <chr>          <dbl>   <dbl>           <dbl> <dbl>   <dbl>   <dbl>       <dbl>
## 1 XOM           0.240   0.0237          0.325  0.101  0.0374 -0.0711      0.0798
## 2 NVDA          0.587   0.0424          0.645  2.27   2.77    1.63        0.867 
## 3 SNAP         -0.474  -0.0298         -0.305  1.10   0.873   2.20        0.350 
## 4 TSLA         -0.218  -0.0101         -0.115  1.82   2.20    3.96        0.646 
## 5 WIX          -0.0463  0.0049          0.0608 1.14   0.334   1.02        0.481 
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## #   `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>

Which stock has a positive distrubtion of returns

RaRb_skewness <- RaRb %>%
    tq_performance(Ra = Ra, 
                   Rb = NULL, 
                   performance_fun = skewness)
RaRb_skewness

## # A tibble: 5 × 2
## # Groups:   symbol [5]
##   symbol skewness.1
##   <chr>       <dbl>
## 1 XOM        0.763 
## 2 NVDA      -0.182 
## 3 SNAP      -0.0816
## 4 TSLA       0.312 
## 5 WIX        0.0477