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("COST", "ELF", "GOOG") %>%
    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: 87 × 3
## # Groups:   symbol [3]
##    symbol date            Ra
##    <chr>  <date>       <dbl>
##  1 COST   2022-01-31 -0.109 
##  2 COST   2022-02-28  0.0295
##  3 COST   2022-03-31  0.109 
##  4 COST   2022-04-29 -0.0751
##  5 COST   2022-05-31 -0.123 
##  6 COST   2022-06-30  0.0280
##  7 COST   2022-07-29  0.131 
##  8 COST   2022-08-31 -0.0355
##  9 COST   2022-09-30 -0.0954
## 10 COST   2022-10-31  0.0638
## # ℹ 77 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: 29 × 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 19 more rows

3 Join the two tables

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

## # A tibble: 87 × 4
## # Groups:   symbol [3]
##    symbol date            Ra      Rb
##    <chr>  <date>       <dbl>   <dbl>
##  1 COST   2022-01-31 -0.109  -0.101 
##  2 COST   2022-02-28  0.0295 -0.0343
##  3 COST   2022-03-31  0.109   0.0341
##  4 COST   2022-04-29 -0.0751 -0.133 
##  5 COST   2022-05-31 -0.123  -0.0205
##  6 COST   2022-06-30  0.0280 -0.0871
##  7 COST   2022-07-29  0.131   0.123 
##  8 COST   2022-08-31 -0.0355 -0.0464
##  9 COST   2022-09-30 -0.0954 -0.105 
## 10 COST   2022-10-31  0.0638  0.0390
## # ℹ 77 more rows

4 Calcualte CAPM

Which of your stock beat the market in 2022?

All of my stocks have a positive Alpha, therefore all of them beat the Market.

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 COST          0.150  0.0126          0.162  0.894   0.507   0.774       0.791
## 2 ELF           1.04   0.0671          1.18   1.02    1.19    0.723       0.495
## 3 GOOG          0.0562 0.0059          0.0726 0.915   1.13    0.432       0.766
## # 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?

None of my stocks are positively skewed.

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

## # A tibble: 3 × 2
## # Groups:   symbol [3]
##   symbol skewness.1
##   <chr>       <dbl>
## 1 COST       -0.408
## 2 ELF        -0.215
## 3 GOOG       -0.295