Apply it to your data 3

Which of your stock beat the market in 2026

1 Get stock prices and convert to returns

Ra <- c("HD", "LOW", "COST") %>%
    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: 27 × 3
## # Groups:   symbol [3]
##    symbol date              Ra
##    <chr>  <date>         <dbl>
##  1 HD     2026-01-30  0.0832  
##  2 HD     2026-02-27  0.0164  
##  3 HD     2026-03-31 -0.130   
##  4 HD     2026-04-30 -0.000274
##  5 HD     2026-05-29 -0.0355  
##  6 HD     2026-06-30  0.120   
##  7 HD     2026-07-31 -0.0588  
##  8 HD     2026-08-31 -0.0124  
##  9 HD     2026-09-21 -0.0868  
## 10 LOW    2026-01-30  0.0866  
## # ℹ 17 more rows

2 Get baseline and convert to returns

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: 27 × 4
## # Groups:   symbol [3]
##    symbol date              Ra       Rb
##    <chr>  <date>         <dbl>    <dbl>
##  1 HD     2026-01-30  0.0832    0.00973
##  2 HD     2026-02-27  0.0164   -0.0338 
##  3 HD     2026-03-31 -0.130    -0.0475 
##  4 HD     2026-04-30 -0.000274  0.153  
##  5 HD     2026-05-29 -0.0355    0.0836 
##  6 HD     2026-06-30  0.120    -0.0281 
##  7 HD     2026-07-31 -0.0588   -0.0320 
##  8 HD     2026-08-31 -0.0124    0.0393 
##  9 HD     2026-09-21 -0.0868    0.0285 
## 10 LOW    2026-01-30  0.0866    0.00973
## # ℹ 17 more rows

4 Calculate CAPM

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

## Registered S3 method overwritten by 'robustbase':
##   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 HD            -0.388 -0.0121     -0.0123          -0.136  0.0281  10.7  
## 2 LOW           -0.504 -0.0261     -0.0262          -0.272  0.0765   6.17 
## 3 COST          -0.154  0.0105      0.0102           0.133 -0.165    0.915
## # 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_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 HD          0.256
## 2 LOW         0.242
## 3 COST        0.623

# All three stocks have a positive distribution with Costco performing the best of the group