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("TLSA", "NVDA", "AMZN") %>%
    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: 135 × 3
## # Groups:   symbol [3]
##    symbol date            Ra
##    <chr>  <date>       <dbl>
##  1 TLSA   2022-01-31 -0.239 
##  2 TLSA   2022-02-28 -0.217 
##  3 TLSA   2022-03-31  0.615 
##  4 TLSA   2022-04-29 -0.0286
##  5 TLSA   2022-05-31 -0.353 
##  6 TLSA   2022-06-30  0.136 
##  7 TLSA   2022-07-29 -0.0267
##  8 TLSA   2022-08-31  0.0411
##  9 TLSA   2022-09-30  0.0263
## 10 TLSA   2022-10-31 -0.103 
## # ℹ 125 more rows

2 Get baselines 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: 45 × 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 35 more rows

3 Join the two tables

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

## # A tibble: 135 × 4
## # Groups:   symbol [3]
##    symbol date            Ra      Rb
##    <chr>  <date>       <dbl>   <dbl>
##  1 TLSA   2022-01-31 -0.239  -0.101 
##  2 TLSA   2022-02-28 -0.217  -0.0343
##  3 TLSA   2022-03-31  0.615   0.0341
##  4 TLSA   2022-04-29 -0.0286 -0.133 
##  5 TLSA   2022-05-31 -0.353  -0.0205
##  6 TLSA   2022-06-30  0.136  -0.0871
##  7 TLSA   2022-07-29 -0.0267  0.123 
##  8 TLSA   2022-08-31  0.0411 -0.0464
##  9 TLSA   2022-09-30  0.0263 -0.105 
## 10 TLSA   2022-10-31 -0.103   0.0390
## # ℹ 125 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 TLSA          0.0244  0.036      -0.0572           0.529 0.513   0.953
## 2 NVDA          0.503   0.0313      0.0282           0.447 2.13    2.87 
## 3 AMZN         -0.0078 -0.0013      0.0026          -0.015 1.37    1.50 
## # 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 positively skewed distribution of returns?

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

## # A tibble: 3 × 2
## # Groups:   symbol [3]
##   symbol VolatilitySkewness.1
##   <chr>                 <dbl>
## 1 TLSA                   3.92
## 2 NVDA                   3.24
## 3 AMZN                   2.12