Apply 3

Which of the chosen stocks beat the market

# Load Packages
library(tidyverse)

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library(tidyquant)

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1 Get Stock Prices & Convert To Returns

Ra <- c("NVDA", "PLTR", "MSFT", "CSCO") %>%
    tq_get(get  = "stock.prices",
           from = "2025-01-01",
           to   = Sys.Date()) %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "weekly", 
                 col_rename = "Ra")
Ra

## # A tibble: 84 × 3
## # Groups:   symbol [4]
##    symbol date            Ra
##    <chr>  <date>       <dbl>
##  1 NVDA   2025-01-03  0.0445
##  2 NVDA   2025-01-10 -0.0593
##  3 NVDA   2025-01-17  0.0132
##  4 NVDA   2025-01-24  0.0357
##  5 NVDA   2025-01-31 -0.158 
##  6 NVDA   2025-02-07  0.0814
##  7 NVDA   2025-02-14  0.0694
##  8 NVDA   2025-02-21 -0.0318
##  9 NVDA   2025-02-28 -0.0707
## 10 NVDA   2025-03-07 -0.0979
## # ℹ 74 more rows

2 Get Baseline & Convert To Returns

Rb <- "^IXIC" %>%
    tq_get(get  = "stock.prices",
           from = "2025-01-01") %>%
    tq_transmute(select = adjusted,
                 mutate_fun = periodReturn, 
                 period     = "weekly", 
                 col_rename = "Rb")
Rb

## # A tibble: 21 × 2
##    date             Rb
##    <date>        <dbl>
##  1 2025-01-03  0.0177 
##  2 2025-01-10 -0.0234 
##  3 2025-01-17  0.0245 
##  4 2025-01-24  0.0165 
##  5 2025-01-31 -0.0164 
##  6 2025-02-07 -0.00530
##  7 2025-02-14  0.0258 
##  8 2025-02-21 -0.0251 
##  9 2025-02-28 -0.0347 
## 10 2025-03-07 -0.0345 
## # ℹ 11 more rows

3 Join the two tables

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

## # A tibble: 84 × 4
## # Groups:   symbol [4]
##    symbol date            Ra       Rb
##    <chr>  <date>       <dbl>    <dbl>
##  1 NVDA   2025-01-03  0.0445  0.0177 
##  2 NVDA   2025-01-10 -0.0593 -0.0234 
##  3 NVDA   2025-01-17  0.0132  0.0245 
##  4 NVDA   2025-01-24  0.0357  0.0165 
##  5 NVDA   2025-01-31 -0.158  -0.0164 
##  6 NVDA   2025-02-07  0.0814 -0.00530
##  7 NVDA   2025-02-14  0.0694  0.0258 
##  8 NVDA   2025-02-21 -0.0318 -0.0251 
##  9 NVDA   2025-02-28 -0.0707 -0.0347 
## 10 NVDA   2025-03-07 -0.0979 -0.0345 
## # ℹ 74 more rows

4 Calculate CAPM

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

## Registered S3 method overwritten by 'robustbase':
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RaRb_capm

## # A tibble: 4 × 18
## # Groups:   symbol [4]
##   symbol ActivePremium  Alpha AlphaRobust AnnualizedAlpha  Beta `Beta-`
##   <chr>          <dbl>  <dbl>       <dbl>           <dbl> <dbl>   <dbl>
## 1 NVDA         -0.0607 0.002       0.0017           0.110 1.81    1.64 
## 2 PLTR          2.27   0.0308      0.0152           3.85  2.07    2.39 
## 3 MSFT          0.276  0.005       0.0039           0.293 0.825   0.402
## 4 CSCO          0.272  0.0047      0.0049           0.273 0.754   1.07 
## # 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>

5 Which stock has a positively skewed distribution of returns?

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

## # A tibble: 4 × 2
## # Groups:   symbol [4]
##   symbol VolatilitySkewness.1
##   <chr>                 <dbl>
## 1 NVDA                  1.26 
## 2 PLTR                  2.84 
## 3 MSFT                  1.36 
## 4 CSCO                  0.946