Apply it to your data 3

Which of your stock beat the market in 2022?

# Load packages 
library(tidyverse)
library(tidyquant)

Get stock prices and convert to returns

Ra <- c ("NKE", "AMZN", "MSFT") %>%
    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 NKE    2022-01-31 -0.101 
##  2 NKE    2022-02-28 -0.0778
##  3 NKE    2022-03-31 -0.0123
##  4 NKE    2022-04-29 -0.0733
##  5 NKE    2022-05-31 -0.0469
##  6 NKE    2022-06-30 -0.138 
##  7 NKE    2022-07-29  0.124 
##  8 NKE    2022-08-31 -0.0737
##  9 NKE    2022-09-30 -0.217 
## 10 NKE    2022-10-31  0.115 
## # 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 NKE    2022-01-31 -0.101  -0.101 
##  2 NKE    2022-02-28 -0.0778 -0.0343
##  3 NKE    2022-03-31 -0.0123  0.0341
##  4 NKE    2022-04-29 -0.0733 -0.133 
##  5 NKE    2022-05-31 -0.0469 -0.0205
##  6 NKE    2022-06-30 -0.138  -0.0871
##  7 NKE    2022-07-29  0.124   0.123 
##  8 NKE    2022-08-31 -0.0737 -0.0464
##  9 NKE    2022-09-30 -0.217  -0.105 
## 10 NKE    2022-10-31  0.115   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 NKE          -0.245  -0.0177         -0.193  0.687   0.718   0.542       0.467
## 2 AMZN         -0.0031  0.0009          0.0109 1.37    1.57    1.85        0.841
## 3 MSFT          0.063   0.006           0.0742 0.825   0.750   0.543       0.806
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## #   `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>

Which stock has a positive 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 NKE        -0.189
## 2 AMZN        0.232
## 3 MSFT        0.104

Amazon and Microsoft are the stocks that have a positively skewed distribution of returns while Nike has a negatively skewed distribution of returns.