Apply it to your data 3

Which of your stock beat the market in 2022

# Load packages
library(tidyverse)
library(tidyquant)
library(moments)

Get stock prices and cnonvert to returns

Ra <- c("MSFT", "SPOT", "DKNG") %>%
    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 MSFT   2022-01-31 -0.0710 
##  2 MSFT   2022-02-28 -0.0372 
##  3 MSFT   2022-03-31  0.0319 
##  4 MSFT   2022-04-29 -0.0999 
##  5 MSFT   2022-05-31 -0.0181 
##  6 MSFT   2022-06-30 -0.0553 
##  7 MSFT   2022-07-29  0.0931 
##  8 MSFT   2022-08-31 -0.0667 
##  9 MSFT   2022-09-30 -0.109  
## 10 MSFT   2022-10-31 -0.00331
## # ℹ 89 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 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 MSFT   2022-01-31 -0.0710  -0.101 
##  2 MSFT   2022-02-28 -0.0372  -0.0343
##  3 MSFT   2022-03-31  0.0319   0.0341
##  4 MSFT   2022-04-29 -0.0999  -0.133 
##  5 MSFT   2022-05-31 -0.0181  -0.0205
##  6 MSFT   2022-06-30 -0.0553  -0.0871
##  7 MSFT   2022-07-29  0.0931   0.123 
##  8 MSFT   2022-08-31 -0.0667  -0.0464
##  9 MSFT   2022-09-30 -0.109   -0.105 
## 10 MSFT   2022-10-31 -0.00331  0.0390
## # ℹ 89 more rows

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 MSFT          0.0697 0.0065          0.0802 0.821   0.855   0.567       0.802
## 2 SPOT          0.0907 0.0115          0.146  1.60    2.63    2.22        0.740
## 3 DKNG          0.0911 0.0143          0.186  1.49    2.77    2.03        0.616
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## #   `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>

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

## # A tibble: 3 × 2
## # Groups:   symbol [3]
##   symbol skewness
##   <chr>     <dbl>
## 1 MSFT     0.104 
## 2 SPOT     0.0484
## 3 DKNG     0.122

Interpret

All three, DKNG, MSFT, and SPOT have positive skewness, it means they usually have small gains with occasional big jumps in price. This shows there’s a chance for large profits, but these are less common. Investors might see these stocks as having good upside potential with fewer big losses.