library(tidyverse)
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library(tidyquant)
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1 Get stock prices and convert to returns

Ra <- c("TSLA", "MSFT", "DPZ") %>%
  tq_get(get = "stock.prices", from = "2022-01-01") %>%
  group_by(symbol) %>%
  tq_transmute(select = adjusted,
               mutate_fun = periodReturn,
               period = "monthly",
               col_rename = "Ra")

2 Get baseline and convert to returns

Rb <- tq_get("^IXIC", get = "stock.prices", from = "2022-01-01") %>%
  tq_transmute(select = adjusted,
               mutate_fun = periodReturn,
               period = "monthly",
               col_rename = "Rb")

3 Join the two tables

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

4 Calculate CAPM

RaRb_capm <- RaRb %>%
  tq_performance(Ra = Ra, Rb = Rb, performance_fun = table.CAPM)
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##   method          from     
##   hatvalues.lmrob RobStatTM
RaRb_capm
## # A tibble: 3 × 18
## # Groups:   symbol [3]
##   symbol ActivePremium   Alpha AlphaRobust AnnualizedAlpha  Beta `Beta-`
##   <chr>          <dbl>   <dbl>       <dbl>           <dbl> <dbl>   <dbl>
## 1 TSLA         -0.122  -0.0021     -0.002          -0.0248 1.99    1.88 
## 2 MSFT          0.0559  0.0054      0.0028          0.0671 0.871   0.635
## 3 DPZ          -0.108  -0.0047     -0.0047         -0.0549 0.839   0.656
## # 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>

Final Results

If we were to look at the stocks individually we would most likely choose Stock number 2 MSFT, as it is currently skewing postive with 0.055 in contrast to the negative skewing stocks showcasing some form of better performace atleast growth wise.