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("HD", "XOM", "AXP") %>%
    tq_get(get  = "stock.prices",
           from = "2022-01-01") %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 col_rename = "Ra")

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")

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 HD     2022-01-31 -0.102   -0.101 
##  2 HD     2022-02-28 -0.139   -0.0343
##  3 HD     2022-03-31 -0.0465   0.0341
##  4 HD     2022-04-29  0.00357 -0.133 
##  5 HD     2022-05-31  0.00782 -0.0205
##  6 HD     2022-06-30 -0.0883  -0.0871
##  7 HD     2022-07-29  0.0972   0.123 
##  8 HD     2022-08-31 -0.0353  -0.0464
##  9 HD     2022-09-20 -0.0494  -0.0331
## 10 XOM    2022-01-31  0.195   -0.101 
## # … with 17 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 ActiveP…¹   Alpha Annua…²  Beta `Beta-` `Beta+` Corre…³ Corre…⁴ Infor…⁵
##   <chr>      <dbl>   <dbl>   <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1 HD        -0.045 -0.0221  -0.235 0.517 -0.0756    1.61   0.564   0.113  -0.189
## 2 XOM        1.07   0.0607   1.03  0.328 -0.0402    0.88   0.267   0.488   2.97 
## 3 AXP        0.248  0.0124   0.159 0.543  0.630     1.68   0.473   0.199   0.845
## # … 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 HD                    0.502
## 2 XOM                3946.   
## 3 AXP                   0.632