1 Import stock prices and convert to returns

Ra <- c("TSLA", "AMZN", "AAPL", "NVDA", "PG") %>%
    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: 225 × 3
## # Groups:   symbol [5]
##    symbol date            Ra
##    <chr>  <date>       <dbl>
##  1 TSLA   2022-01-31 -0.219 
##  2 TSLA   2022-02-28 -0.0708
##  3 TSLA   2022-03-31  0.238 
##  4 TSLA   2022-04-29 -0.192 
##  5 TSLA   2022-05-31 -0.129 
##  6 TSLA   2022-06-30 -0.112 
##  7 TSLA   2022-07-29  0.324 
##  8 TSLA   2022-08-31 -0.0725
##  9 TSLA   2022-09-30 -0.0376
## 10 TSLA   2022-10-31 -0.142 
## # ℹ 215 more rows

2 Get baseline and convert 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: 45 × 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
## # ℹ 35 more rows

3 Join the two tables

RaRb <- left_join(Ra, Rb, by = c("date" = "date"))
RaRb
## # A tibble: 225 × 4
## # Groups:   symbol [5]
##    symbol date            Ra      Rb
##    <chr>  <date>       <dbl>   <dbl>
##  1 TSLA   2022-01-31 -0.219  -0.101 
##  2 TSLA   2022-02-28 -0.0708 -0.0343
##  3 TSLA   2022-03-31  0.238   0.0341
##  4 TSLA   2022-04-29 -0.192  -0.133 
##  5 TSLA   2022-05-31 -0.129  -0.0205
##  6 TSLA   2022-06-30 -0.112  -0.0871
##  7 TSLA   2022-07-29  0.324   0.123 
##  8 TSLA   2022-08-31 -0.0725 -0.0464
##  9 TSLA   2022-09-30 -0.0376 -0.105 
## 10 TSLA   2022-10-31 -0.142   0.0390
## # ℹ 215 more rows

4 Calculate CAPM

RaRb_capm <- RaRb %>%
    tq_performance(Ra = Ra, 
                   Rb = Rb, 
                   performance_fun = table.CAPM)
RaRb_capm
## # A tibble: 5 × 13
## # Groups:   symbol [5]
##   symbol ActivePremium   Alpha AnnualizedAlpha  Beta `Beta-` `Beta+` Correlation
##   <chr>          <dbl>   <dbl>           <dbl> <dbl>   <dbl>   <dbl>       <dbl>
## 1 TSLA         -0.0829 -0.0013         -0.0151 1.96    1.88    3.10        0.645
## 2 AMZN         -0.0148 -0.0018         -0.0216 1.36    1.50    1.69        0.844
## 3 AAPL         -0.0112  0.001           0.0117 0.893   0.967   0.995       0.739
## 4 NVDA          0.504   0.031           0.442  2.12    2.87    2.11        0.831
## 5 PG           -0.0865  0.0013          0.0153 0.110  -0.196  -0.232       0.133
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## #   `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>

Which stock has a positively skewed distribution of returns?

Skewness

RaRb_skewness <- RaRb %>%
    tq_performance(Ra = Ra, 
                   Rb = NULL, 
                   performance_fun = skewness)
RaRb_skewness
## # A tibble: 5 × 2
## # Groups:   symbol [5]
##   symbol skewness.1
##   <chr>       <dbl>
## 1 TSLA        0.221
## 2 AMZN        0.133
## 3 AAPL        0.270
## 4 NVDA       -0.118
## 5 PG          0.139

Which stock(s) beat the market in 2022?

Based on the results that are showed above, in 2022, the following stocks beat the market: TSLA, AMAZN, APPL, and PG.