Apply it to your data 3

Which of your stocks beat the market in 2022?

# Load Packages
library(tidyverse)
library(tidyquant)

1 Get stock prices and concert to returns

Ra <- c("CELH", "QTUM", "NOK") %>%
    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: 171 × 3
## # Groups:   symbol [3]
##    symbol date             Ra
##    <chr>  <date>        <dbl>
##  1 CELH   2022-01-31 -0.364  
##  2 CELH   2022-02-28  0.339  
##  3 CELH   2022-03-31 -0.136  
##  4 CELH   2022-04-29 -0.0576 
##  5 CELH   2022-05-31  0.290  
##  6 CELH   2022-06-30 -0.0273 
##  7 CELH   2022-07-29  0.363  
##  8 CELH   2022-08-31  0.163  
##  9 CELH   2022-09-30 -0.124  
## 10 CELH   2022-10-31  0.00441
## # ℹ 161 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: 57 × 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 47 more rows

3 Join the two tables

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

## # A tibble: 171 × 4
## # Groups:   symbol [3]
##    symbol date             Ra      Rb
##    <chr>  <date>        <dbl>   <dbl>
##  1 CELH   2022-01-31 -0.364   -0.101 
##  2 CELH   2022-02-28  0.339   -0.0343
##  3 CELH   2022-03-31 -0.136    0.0341
##  4 CELH   2022-04-29 -0.0576  -0.133 
##  5 CELH   2022-05-31  0.290   -0.0205
##  6 CELH   2022-06-30 -0.0273  -0.0871
##  7 CELH   2022-07-29  0.363    0.123 
##  8 CELH   2022-08-31  0.163   -0.0464
##  9 CELH   2022-09-30 -0.124   -0.105 
## 10 CELH   2022-10-31  0.00441  0.0390
## # ℹ 161 more rows

4 Calculate CAPM

RaRb_capm <- RaRb %>%
    tq_performance(Ra = Ra,
                   Rb = Rb,
                   performance_fun = table.CAPM)
RaRb_capm

## # A tibble: 3 × 18
## # Groups:   symbol [3]
##   symbol ActivePremium  Alpha AlphaRobust AnnualizedAlpha  Beta `Beta-`
##   <chr>          <dbl>  <dbl>       <dbl>           <dbl> <dbl>   <dbl>
## 1 CELH         -0.0959 0.0117     -0.0199           0.150 0.726   0.804
## 2 QTUM          0.126  0.0084      0.0079           0.106 1.18    0.981
## 3 NOK           0.0293 0.0083      0.0032           0.104 1.01    0.655
## # ℹ 11 more variables: `Beta-Robust` <dbl>, `Beta+` <dbl>, `Beta+Robust` <dbl>,
## #   BetaRobust <dbl>, Correlation <dbl>, `Correlationp-value` <dbl>,
## #   InformationRatio <dbl>, `R-squared` <dbl>, `R-squaredRobust` <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 CELH        0.695
## 2 QTUM        0.218
## 3 NOK         1.61