Apply it to your data 3

Stocks that beat the market in 2022

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

1. Get stock prices and convert to returns

Ra <- c("X", "CMC", "ZEUS") %>%
    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 X      2022-01-31 -0.134 
##  2 X      2022-02-28  0.316 
##  3 X      2022-03-31  0.387 
##  4 X      2022-04-29 -0.192 
##  5 X      2022-05-31 -0.176 
##  6 X      2022-06-30 -0.286 
##  7 X      2022-07-29  0.320 
##  8 X      2022-08-31 -0.0309
##  9 X      2022-09-30 -0.208 
## 10 X      2022-10-31  0.124 
## # 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 X      2022-01-31 -0.134  -0.101 
##  2 X      2022-02-28  0.316  -0.0343
##  3 X      2022-03-31  0.387   0.0341
##  4 X      2022-04-29 -0.192  -0.133 
##  5 X      2022-05-31 -0.176  -0.0205
##  6 X      2022-06-30 -0.286  -0.0871
##  7 X      2022-07-29  0.320   0.123 
##  8 X      2022-08-31 -0.0309 -0.0464
##  9 X      2022-09-30 -0.208  -0.105 
## 10 X      2022-10-31  0.124   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 X              0.132 0.0202           0.270 1.36    2.44     1.89       0.504
## 2 CMC            0.111 0.0123           0.158 0.958   0.701    1.06       0.567
## 3 ZEUS           0.170 0.0224           0.304 1.16    1.83     1.45       0.456
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## #   `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>

Which stock has a positively skewed distribution of returns?

RaRb_scew <- RaRb %>%
    tq_performance(Ra = Ra,
                   Rb = Rb,
                   performance_fun = CoSkewness)
RaRb_scew

## # A tibble: 3 × 2
## # Groups:   symbol [3]
##   symbol CoSkewness.1
##   <chr>         <dbl>
## 1 X        -0.000163 
## 2 CMC      -0.0000242
## 3 ZEUS     -0.000111