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library(tidyquant)
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Get stock prices and covert to returns

Ra <- c("XOM", "TSLA", "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: 469 × 3
## # Groups:   symbol [3]
##    symbol date             Ra
##    <chr>  <date>        <dbl>
##  1 XOM    2010-01-29 -0.0683 
##  2 XOM    2010-02-26  0.0154 
##  3 XOM    2010-03-31  0.0305 
##  4 XOM    2010-04-30  0.0118 
##  5 XOM    2010-05-28 -0.102  
##  6 XOM    2010-06-30 -0.0561 
##  7 XOM    2010-07-30  0.0457 
##  8 XOM    2010-08-31 -0.00246
##  9 XOM    2010-09-30  0.0453 
## 10 XOM    2010-10-29  0.0761 
## # … with 459 more rows

Get baseline and covnert to returns

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: 14 × 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 2023-02-17  0.0175

Join the two tables

RaRb <- left_join(Ra, Rb, by = c("date" = "date"))
RaRb
## # A tibble: 469 × 4
## # Groups:   symbol [3]
##    symbol date             Ra    Rb
##    <chr>  <date>        <dbl> <dbl>
##  1 XOM    2010-01-29 -0.0683     NA
##  2 XOM    2010-02-26  0.0154     NA
##  3 XOM    2010-03-31  0.0305     NA
##  4 XOM    2010-04-30  0.0118     NA
##  5 XOM    2010-05-28 -0.102      NA
##  6 XOM    2010-06-30 -0.0561     NA
##  7 XOM    2010-07-30  0.0457     NA
##  8 XOM    2010-08-31 -0.00246    NA
##  9 XOM    2010-09-30  0.0453     NA
## 10 XOM    2010-10-29  0.0761     NA
## # … with 459 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 ActivePr…¹  Alpha Annua…²  Beta `Beta-` `Beta+` Corre…³ Corre…⁴ Infor…⁵
##   <chr>       <dbl>  <dbl>   <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1 XOM        0.961  0.0594  0.998  0.398   0.526   0.490   0.292  0.311    2.39 
## 2 TSLA      -0.139  0.0207  0.279  2.01    0.669   3.13    0.737  0.0026  -0.236
## 3 DIS       -0.0586 0.0016  0.0194 1.22    2.09    2.25    0.78   0.001   -0.210
## # … 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

Which stock has a posivley scewed distrabution of returns?

All three of them have positivly scewed distrabution

RaRb_capm <- RaRb %>%
    tq_performance(Ra = Ra, 
                   Rb = NULL, 
                   performance_fun = skewness)
RaRb_capm
## # A tibble: 3 × 2
## # Groups:   symbol [3]
##   symbol skewness.1
##   <chr>       <dbl>
## 1 XOM         0.385
## 2 TSLA        1.28 
## 3 DIS         0.257