Apply it to your data 3

Which of your stocks beat the market in 2022

# Load packages
library(tidyverse)
library(tidyquant)

1 Get stock prices and convert to returns

Ra <- c("ADDYY", "NKE", "WMT") %>%
  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: 171 × 3
## # Groups:   symbol [3]
##    symbol date             Ra
##    <chr>  <date>        <dbl>
##  1 ADDYY  2022-01-31 -0.0722 
##  2 ADDYY  2022-02-28 -0.137  
##  3 ADDYY  2022-03-31 -0.00866
##  4 ADDYY  2022-04-29 -0.143  
##  5 ADDYY  2022-05-31  0.0105 
##  6 ADDYY  2022-06-30 -0.106  
##  7 ADDYY  2022-07-29 -0.0278 
##  8 ADDYY  2022-08-31 -0.139  
##  9 ADDYY  2022-09-30 -0.226  
## 10 ADDYY  2022-10-31 -0.151  
## # ℹ 161 more rows

Get baseline and convert to returns

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

## # A tibble: 57 × 3
## # Groups:   symbol [1]
##    symbol date            Rb
##    <chr>  <date>       <dbl>
##  1 ^IXIC  2022-01-31 -0.101 
##  2 ^IXIC  2022-02-28 -0.0343
##  3 ^IXIC  2022-03-31  0.0341
##  4 ^IXIC  2022-04-29 -0.133 
##  5 ^IXIC  2022-05-31 -0.0205
##  6 ^IXIC  2022-06-30 -0.0871
##  7 ^IXIC  2022-07-29  0.123 
##  8 ^IXIC  2022-08-31 -0.0464
##  9 ^IXIC  2022-09-30 -0.105 
## 10 ^IXIC  2022-10-31  0.0390
## # ℹ 47 more rows

3 Join the two tables

RaRb <- left_join(Ra, Rb, by = "date") %>%
  select(symbol.x, date, Ra, Rb)

4 Calculate CAPM

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

## # A tibble: 3 × 18
## # Groups:   symbol.x [3]
##   symbol.x ActivePremium   Alpha AlphaRobust AnnualizedAlpha  Beta `Beta-`
##   <chr>            <dbl>   <dbl>       <dbl>           <dbl> <dbl>   <dbl>
## 1 ADDYY          -0.225  -0.0136     -0.0173          -0.151 0.899   1.13 
## 2 NKE            -0.380  -0.027      -0.0206          -0.280 0.581   0.796
## 3 WMT             0.0784  0.0132      0.0186           0.170 0.335   0.369
## # 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 %>%
  group_by(symbol.x) %>%
  tq_performance(
    Ra = Ra,
    Rb = Rb,
    performance_fun = table.HigherMoments, 
    )
RaRb_capm

## # A tibble: 3 × 6
## # Groups:   symbol.x [3]
##   symbol.x BetaCoKurtosis BetaCoSkewness BetaCoVariance CoKurtosis CoSkewness
##   <chr>             <dbl>          <dbl>          <dbl>      <dbl>      <dbl>
## 1 ADDYY             0.894           1.07          0.899          0     0     
## 2 NKE               0.449           2.73          0.581          0    -0.0001
## 3 WMT               0.276           1.65          0.335          0     0

RaRb %>%
  group_by(symbol.x) %>%
  summarise(
    Skewness = skewness(Ra, na.rm = TRUE)
  )

## # A tibble: 3 × 2
##   symbol.x Skewness
##   <chr>       <dbl>
## 1 ADDYY      0.465 
## 2 NKE       -0.0511
## 3 WMT       -0.374