Apply it to your data 3

Which of your stock beat the market in 2022

Load Packages

library(tidyverse)
library(tidyquant)

1. Get Stock Prices & Convert to Monthly Returns

Ra <- c("V", "AMZN", "NFLX", "WMT", "KO") %>%
  tq_get(get = "stock.prices",
      from = "2022-01-01", to = Sys.Date()) %>%
  group_by(symbol) %>%
  tq_transmute(
    select     = adjusted,
    mutate_fun = periodReturn,
    period     = "monthly",
    col_rename = "Ra"
  )

2. Get Baseline (XLK) & Convert to Monthly Returns

Rb <- "^IXIC" %>%
  tq_get(get = "stock.prices", from = "2022-01-01", to = Sys.Date()) %>%
  tq_transmute(
    select     = adjusted,
    mutate_fun = periodReturn,
    period     = "monthly",
    col_rename = "Rb"
  )

3. Join the Two Tables

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

4. Calculate CAPM

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

## # A tibble: 5 × 18
## # Groups:   symbol [5]
##   symbol ActivePremium  Alpha AlphaRobust AnnualizedAlpha   Beta `Beta-`
##   <chr>          <dbl>  <dbl>       <dbl>           <dbl>  <dbl>   <dbl>
## 1 V             0.105  0.0103      0.0072          0.131  0.553    0.62 
## 2 AMZN         -0.001  0.0007      0.005           0.008  1.39     1.50 
## 3 NFLX          0.172  0.018       0.0278          0.239  1.68     2.24 
## 4 WMT           0.191  0.0172      0.0228          0.228  0.469    0.166
## 5 KO            0.0376 0.0075      0.0061          0.0938 0.0776  -0.142
## # 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>

Which stock has a positively Skewed distribution of returns?

RaRb_stats <- RaRb %>%
  tq_performance(
    Ra = Ra,
    Rb = NULL,
    performance_fun = table.Stats
  )

RaRb_stats

## # A tibble: 5 × 17
## # Groups:   symbol [5]
##   symbol ArithmeticMean GeometricMean Kurtosis `LCLMean(0.95)` Maximum Median
##   <chr>           <dbl>         <dbl>    <dbl>           <dbl>   <dbl>  <dbl>
## 1 V              0.0136        0.0121   0.0255         -0.004    0.166 0.0162
## 2 AMZN           0.009         0.004    0.252          -0.023    0.271 0.0209
## 3 NFLX           0.0281        0.0169   3.16           -0.0165   0.286 0.0372
## 4 WMT            0.0201        0.0182   0.424           0.0006   0.129 0.0171
## 5 KO             0.008         0.0071   0.620          -0.0056   0.122 0.0096
## # ℹ 10 more variables: Minimum <dbl>, NAs <dbl>, Observations <dbl>,
## #   Quartile1 <dbl>, Quartile3 <dbl>, SEMean <dbl>, Skewness <dbl>,
## #   Stdev <dbl>, `UCLMean(0.95)` <dbl>, Variance <dbl>