Performance Analysis with tidyquant

# Load packages
library(tidyverse)
library(tidyquant)

1 Get stock prices and convert to returns

Ra <- c("NKE", "GE", "DIS") %>%
    tq_get(get  = "stock.prices",
           from = "2010-01-01") %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 col_rename = "Ra")
Ra

## # A tibble: 495 × 3
## # Groups:   symbol [3]
##    symbol date            Ra
##    <chr>  <date>       <dbl>
##  1 NKE    2010-01-29 -0.0245
##  2 NKE    2010-02-26  0.0604
##  3 NKE    2010-03-31  0.0916
##  4 NKE    2010-04-30  0.0328
##  5 NKE    2010-05-28 -0.0465
##  6 NKE    2010-06-30 -0.0633
##  7 NKE    2010-07-30  0.0902
##  8 NKE    2010-08-31 -0.0494
##  9 NKE    2010-09-30  0.149 
## 10 NKE    2010-10-29  0.0162
## # ℹ 485 more rows

2 Get baseline and convert to returns

Rb <- "^IXIC" %>%
    tq_get(get  = "stock.prices",
           from = "2010-01-01") %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 col_rename = "Rb")
Rb

## # A tibble: 165 × 2
##    date            Rb
##    <date>       <dbl>
##  1 2010-01-29 -0.0698
##  2 2010-02-26  0.0423
##  3 2010-03-31  0.0714
##  4 2010-04-30  0.0264
##  5 2010-05-28 -0.0829
##  6 2010-06-30 -0.0655
##  7 2010-07-30  0.0690
##  8 2010-08-31 -0.0624
##  9 2010-09-30  0.120 
## 10 2010-10-29  0.0586
## # ℹ 155 more rows

3 Join the two tables

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

## # A tibble: 495 × 4
## # Groups:   symbol [3]
##    symbol date            Ra      Rb
##    <chr>  <date>       <dbl>   <dbl>
##  1 NKE    2010-01-29 -0.0245 -0.0698
##  2 NKE    2010-02-26  0.0604  0.0423
##  3 NKE    2010-03-31  0.0916  0.0714
##  4 NKE    2010-04-30  0.0328  0.0264
##  5 NKE    2010-05-28 -0.0465 -0.0829
##  6 NKE    2010-06-30 -0.0633 -0.0655
##  7 NKE    2010-07-30  0.0902  0.0690
##  8 NKE    2010-08-31 -0.0494 -0.0624
##  9 NKE    2010-09-30  0.149   0.120 
## 10 NKE    2010-10-29  0.0162  0.0586
## # ℹ 485 more rows

4 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 ActivePremium   Alpha AnnualizedAlpha  Beta `Beta-` `Beta+` Correlation
##   <chr>          <dbl>   <dbl>           <dbl> <dbl>   <dbl>   <dbl>       <dbl>
## 1 NKE           0.0118  0.0049          0.0601 0.772   0.877   0.790       0.548
## 2 GE           -0.0985 -0.0042         -0.0492 0.955   1.18    1.05        0.524
## 3 DIS          -0.0567 -0.0025         -0.0297 0.979   1.22    0.934       0.652
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## #   `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>

Which stock has a positively skewed distrabution of return?

RaRb_capm <- RaRb %>%
    tq_performance(Ra = Ra, 
                   Rb = Rb, 
                   performance_fun = CoSkewness)
RaRb_capm

## # A tibble: 3 × 2
## # Groups:   symbol [3]
##   symbol CoSkewness.1
##   <chr>         <dbl>
## 1 NKE      -0.0000248
## 2 GE       -0.0000399
## 3 DIS      -0.0000416