#load Packages
library(tidyquant)
## Loading required package: lubridate
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## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
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## date, intersect, setdiff, union
## Loading required package: PerformanceAnalytics
## Loading required package: xts
## Loading required package: zoo
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## Attaching package: 'zoo'
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## as.Date, as.Date.numeric
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## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
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## legend
## Loading required package: quantmod
## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
library(tidyverse)
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1 Get Stock prices and convert to returns
Ra <- c("AAPL", "GOOG", "NFLX") %>%
tq_get(get = "stock.prices",
from = "2010-01-01",
to = "2015-12-31") %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
col_rename = "Ra")
Ra
## # A tibble: 216 × 3
## # Groups: symbol [3]
## symbol date Ra
## <chr> <date> <dbl>
## 1 AAPL 2010-01-29 -0.103
## 2 AAPL 2010-02-26 0.0654
## 3 AAPL 2010-03-31 0.148
## 4 AAPL 2010-04-30 0.111
## 5 AAPL 2010-05-28 -0.0161
## 6 AAPL 2010-06-30 -0.0208
## 7 AAPL 2010-07-30 0.0227
## 8 AAPL 2010-08-31 -0.0550
## 9 AAPL 2010-09-30 0.167
## 10 AAPL 2010-10-29 0.0607
## # … with 206 more rows
2 Get baseline and convert to returns
Rb <- "XLK" %>%
tq_get(get = "stock.prices",
from = "2010-01-01",
to = "2015-12-31") %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
col_rename = "Rb")
Rb
## # A tibble: 72 × 2
## date Rb
## <date> <dbl>
## 1 2010-01-29 -0.0993
## 2 2010-02-26 0.0348
## 3 2010-03-31 0.0684
## 4 2010-04-30 0.0126
## 5 2010-05-28 -0.0748
## 6 2010-06-30 -0.0540
## 7 2010-07-30 0.0745
## 8 2010-08-31 -0.0561
## 9 2010-09-30 0.117
## 10 2010-10-29 0.0578
## # … with 62 more rows
3 Join the two data tables
RaRb <- left_join(Ra, Rb, by = c("date" = "date"))
RaRb
## # A tibble: 216 × 4
## # Groups: symbol [3]
## symbol date Ra Rb
## <chr> <date> <dbl> <dbl>
## 1 AAPL 2010-01-29 -0.103 -0.0993
## 2 AAPL 2010-02-26 0.0654 0.0348
## 3 AAPL 2010-03-31 0.148 0.0684
## 4 AAPL 2010-04-30 0.111 0.0126
## 5 AAPL 2010-05-28 -0.0161 -0.0748
## 6 AAPL 2010-06-30 -0.0208 -0.0540
## 7 AAPL 2010-07-30 0.0227 0.0745
## 8 AAPL 2010-08-31 -0.0550 -0.0561
## 9 AAPL 2010-09-30 0.167 0.117
## 10 AAPL 2010-10-29 0.0607 0.0578
## # … with 206 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 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 AAPL 0.119 0.0089 0.112 1.11 0.578 1.04 0.659 0 0.628
## 2 GOOG 0.034 0.0028 0.034 1.14 1.39 1.16 0.644 0 0.168
## 3 NFLX 0.447 0.053 0.859 0.384 -1.52 0.0045 0.0817 0.495 0.637
## # … with 3 more variables: `R-squared` <dbl>, TrackingError <dbl>,
## # TreynorRatio <dbl>, and abbreviated variable names ¹ActivePremium,
## # ²AnnualizedAlpha, ³Correlation, ⁴`Correlationp-value`, ⁵InformationRatio