Apply it to your data 3

Which of your stock beat the market in 2024?

# load packages
library(tidyverse)
library(tidyquant)

get stock prices and convert to returns

Ra <- c("AMD", "NVDA", "INTU") %>%
    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 AMD    2022-01-31 -0.240 
##  2 AMD    2022-02-28  0.0796
##  3 AMD    2022-03-31 -0.114 
##  4 AMD    2022-04-29 -0.218 
##  5 AMD    2022-05-31  0.191 
##  6 AMD    2022-06-30 -0.249 
##  7 AMD    2022-07-29  0.235 
##  8 AMD    2022-08-31 -0.102 
##  9 AMD    2022-09-30 -0.253 
## 10 AMD    2022-10-31 -0.0521
## # 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 AMD    2022-01-31 -0.240  -0.101 
##  2 AMD    2022-02-28  0.0796 -0.0343
##  3 AMD    2022-03-31 -0.114   0.0341
##  4 AMD    2022-04-29 -0.218  -0.133 
##  5 AMD    2022-05-31  0.191  -0.0205
##  6 AMD    2022-06-30 -0.249  -0.0871
##  7 AMD    2022-07-29  0.235   0.123 
##  8 AMD    2022-08-31 -0.102  -0.0464
##  9 AMD    2022-09-30 -0.253  -0.105 
## 10 AMD    2022-10-31 -0.0521  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 AMD          -0.033   0.0028          0.0336  1.89   2.53     2.06       0.754
## 2 NVDA          0.587   0.0424          0.645   2.27   2.77     1.63       0.867
## 3 INTU         -0.0324 -0.0013         -0.0156  1.02   0.892    1.28       0.773
## # 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? INTU has a positively skewed distribution of returns

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

## # A tibble: 3 × 2
## # Groups:   symbol [3]
##   symbol skewness.1
##   <chr>       <dbl>
## 1 AMD         0.174
## 2 NVDA       -0.182
## 3 INTU        0.312