Apply 3

Which Stocks Beat the Market in 2022

## Load Packages
library(tidyverse)
library(tidyquant)

Get Stock prices and Convert to Return

Ra <- c("MSFT", "HD", "UPS") %>%
    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: 45 × 3
## # Groups:   symbol [3]
##    symbol date             Ra
##    <chr>  <date>        <dbl>
##  1 MSFT   2022-01-31 -0.0710 
##  2 MSFT   2022-02-28 -0.0372 
##  3 MSFT   2022-03-31  0.0319 
##  4 MSFT   2022-04-29 -0.0999 
##  5 MSFT   2022-05-31 -0.0181 
##  6 MSFT   2022-06-30 -0.0553 
##  7 MSFT   2022-07-29  0.0931 
##  8 MSFT   2022-08-31 -0.0667 
##  9 MSFT   2022-09-30 -0.109  
## 10 MSFT   2022-10-31 -0.00331
## # … with 35 more rows

Get Baseline and Convert to Return

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: 15 × 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
## 11 2022-11-30  0.0437
## 12 2022-12-30 -0.0873
## 13 2023-01-31  0.107 
## 14 2023-02-28 -0.0111
## 15 2023-03-08  0.0105

Join the Two Data Tables

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

## # A tibble: 45 × 4
## # Groups:   symbol [3]
##    symbol date             Ra      Rb
##    <chr>  <date>        <dbl>   <dbl>
##  1 MSFT   2022-01-31 -0.0710  -0.101 
##  2 MSFT   2022-02-28 -0.0372  -0.0343
##  3 MSFT   2022-03-31  0.0319   0.0341
##  4 MSFT   2022-04-29 -0.0999  -0.133 
##  5 MSFT   2022-05-31 -0.0181  -0.0205
##  6 MSFT   2022-06-30 -0.0553  -0.0871
##  7 MSFT   2022-07-29  0.0931   0.123 
##  8 MSFT   2022-08-31 -0.0667  -0.0464
##  9 MSFT   2022-09-30 -0.109   -0.105 
## 10 MSFT   2022-10-31 -0.00331  0.0390
## # … with 35 more rows

Calculate CAPM

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

## # A tibble: 3 × 13
## # Groups:   symbol [3]
##   symbol ActiveP…¹   Alpha Annua…²  Beta `Beta-` `Beta+` Corre…³ Corre…⁴ Infor…⁵
##   <chr>      <dbl>   <dbl>   <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1 MSFT      0.0303 -0.0023 -0.0272 0.747   0.780   0.428   0.903  0        0.258
## 2 HD        0.0046 -0.0078 -0.0898 0.557  -0.264   0.599   0.604  0.0171   0.02 
## 3 UPS       0.140   0.0116  0.149  0.855   1.51    0.338   0.794  0.0004   0.779
## # … with 3 more variables: `R-squared` <dbl>, TrackingError <dbl>,
## #   TreynorRatio <dbl>, and abbreviated variable names ¹​ActivePremium,
## #   ²​AnnualizedAlpha, ³​Correlation, ⁴​`Correlationp-value`, ⁵​InformationRatio

Whick Stock has a positive skewed distrubution 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 MSFT                   1.19
## 2 HD                     1.00
## 3 UPS                    1.72