Apply to you data 3

Which or my stocks beat the market in 2022

# Load packages
library(tidyverse)
library(tidyquant)

1 Get stock prices and convert to returns

Ra <- c("AMZN", "TSLA", "NFLX") %>%
    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: 42 × 3
## # Groups:   symbol [3]
##    symbol date            Ra
##    <chr>  <date>       <dbl>
##  1 AMZN   2022-01-31 -0.122 
##  2 AMZN   2022-02-28  0.0267
##  3 AMZN   2022-03-31  0.0614
##  4 AMZN   2022-04-29 -0.238 
##  5 AMZN   2022-05-31 -0.0328
##  6 AMZN   2022-06-30 -0.116 
##  7 AMZN   2022-07-29  0.271 
##  8 AMZN   2022-08-31 -0.0606
##  9 AMZN   2022-09-30 -0.109 
## 10 AMZN   2022-10-31 -0.0935
## # … with 32 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: 14 × 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
## 11 2022-11-30  0.0437
## 12 2022-12-30 -0.0873
## 13 2023-01-31  0.107 
## 14 2023-02-08  0.0281

3 Join the two tables

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

## # A tibble: 42 × 4
## # Groups:   symbol [3]
##    symbol date            Ra      Rb
##    <chr>  <date>       <dbl>   <dbl>
##  1 AMZN   2022-01-31 -0.122  -0.101 
##  2 AMZN   2022-02-28  0.0267 -0.0343
##  3 AMZN   2022-03-31  0.0614  0.0341
##  4 AMZN   2022-04-29 -0.238  -0.133 
##  5 AMZN   2022-05-31 -0.0328 -0.0205
##  6 AMZN   2022-06-30 -0.116  -0.0871
##  7 AMZN   2022-07-29  0.271   0.123 
##  8 AMZN   2022-08-31 -0.0606 -0.0464
##  9 AMZN   2022-09-30 -0.109  -0.105 
## 10 AMZN   2022-10-31 -0.0935  0.0390
## # … with 32 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 AMZN      -0.15  -0.0038 -0.0451  1.46   1.82     3.40   0.857  0.0001  -0.537
## 2 TSLA      -0.228  0.0103  0.131   2.10   0.908    3.67   0.765  0.0014  -0.390
## 3 NFLX      -0.125  0.0221  0.300   1.96   3.12     2.33   0.788  0.0008  -0.246
## # … 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?

For me AMZN and TSLA are poitivley 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 AMZN        0.966
## 2 TSLA        0.656
## 3 NFLX       -0.779