#load packes
library(tidyquant)
library(tidyverse)

1 get stock prices and convert to returns

Ra <- c("NXPI", "IFX.DE") %>%
  tq_get(get = "stock.prices", from = "2026-01-01") %>%
  group_by(symbol) %>%
  tq_transmute(
    select = adjusted,
    mutate_fun = periodReturn,
    period = "monthly",
    col_rename = "Ra"
  )

Ra
## # A tibble: 18 × 3
## # Groups:   symbol [2]
##    symbol date             Ra
##    <chr>  <date>        <dbl>
##  1 NXPI   2026-01-30  0.0220 
##  2 NXPI   2026-02-27  0.00385
##  3 NXPI   2026-03-31 -0.128  
##  4 NXPI   2026-04-30  0.491  
##  5 NXPI   2026-05-29  0.0946 
##  6 NXPI   2026-06-30 -0.123  
##  7 NXPI   2026-07-31 -0.185  
##  8 NXPI   2026-08-31 -0.0197 
##  9 NXPI   2026-09-21  0.0392 
## 10 IFX.DE 2026-01-30  0.0874 
## 11 IFX.DE 2026-02-27  0.110  
## 12 IFX.DE 2026-03-31 -0.171  
## 13 IFX.DE 2026-04-30  0.503  
## 14 IFX.DE 2026-05-29  0.420  
## 15 IFX.DE 2026-06-30  0.00690
## 16 IFX.DE 2026-07-31 -0.245  
## 17 IFX.DE 2026-08-31 -0.0872 
## 18 IFX.DE 2026-09-22  0.0682

2 get baseline and convert to return

Rb <- "^IXIC" %>%
    tq_get(get = "stock.prices",
           from = "2026-01-01") %>%
    tq_transmute(select = adjusted,
                 mutate_fun = periodReturn,
                 period = "monthly",
                 col_rename = "Rb")
Rb
## # A tibble: 9 × 2
##   date             Rb
##   <date>        <dbl>
## 1 2026-01-30  0.00973
## 2 2026-02-27 -0.0338 
## 3 2026-03-31 -0.0475 
## 4 2026-04-30  0.153  
## 5 2026-05-29  0.0836 
## 6 2026-06-30 -0.0281 
## 7 2026-07-31 -0.0320 
## 8 2026-08-31  0.0393 
## 9 2026-09-21  0.0285

3 join the two tables

RaRb <- left_join(Ra, Rb, by = c("date" = "date"))
RaRb
## # A tibble: 18 × 4
## # Groups:   symbol [2]
##    symbol date             Ra       Rb
##    <chr>  <date>        <dbl>    <dbl>
##  1 NXPI   2026-01-30  0.0220   0.00973
##  2 NXPI   2026-02-27  0.00385 -0.0338 
##  3 NXPI   2026-03-31 -0.128   -0.0475 
##  4 NXPI   2026-04-30  0.491    0.153  
##  5 NXPI   2026-05-29  0.0946   0.0836 
##  6 NXPI   2026-06-30 -0.123   -0.0281 
##  7 NXPI   2026-07-31 -0.185   -0.0320 
##  8 NXPI   2026-08-31 -0.0197   0.0393 
##  9 NXPI   2026-09-21  0.0392   0.0285 
## 10 IFX.DE 2026-01-30  0.0874   0.00973
## 11 IFX.DE 2026-02-27  0.110   -0.0338 
## 12 IFX.DE 2026-03-31 -0.171   -0.0475 
## 13 IFX.DE 2026-04-30  0.503    0.153  
## 14 IFX.DE 2026-05-29  0.420    0.0836 
## 15 IFX.DE 2026-06-30  0.00690 -0.0281 
## 16 IFX.DE 2026-07-31 -0.245   -0.0320 
## 17 IFX.DE 2026-08-31 -0.0872   0.0393 
## 18 IFX.DE 2026-09-22  0.0682  NA

4 calculate CAPM

RaRb_capm <- RaRb %>%
    tq_performance(Ra = Ra,
                   Rb = Rb,
                   performance_fun = table.CAPM)
RaRb_capm
## # A tibble: 2 × 18
## # Groups:   symbol [2]
##   symbol ActivePremium   Alpha AlphaRobust AnnualizedAlpha  Beta `Beta-`
##   <chr>          <dbl>   <dbl>       <dbl>           <dbl> <dbl>   <dbl>
## 1 NXPI          -0.143 -0.0307     -0.0309          -0.312  2.74   0.280
## 2 IFX.DE         0.596  0.0207      0.0208           0.279  3.18   6.91 
## # 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 skwed distrubution of return?

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

RaRb_skewness
## # A tibble: 2 × 2
## # Groups:   symbol [2]
##   symbol skewness.1
##   <chr>       <dbl>
## 1 NXPI        1.51 
## 2 IFX.DE      0.552

Both stocks have positively skewed returns because their skewness values are above zero. NXPI is more positively skewed (1.51) than IFX.DE (0.552).