# Load packages
library(tidyverse)
library(tidyquant)Ra <- c("JPM", "MS", "DNB.OL", "NDA-FI.HE") %>%
tq_get(get = "stock.prices",
from = "2022-01-01") %>%
group_by(symbol) %>% # Group by symbol to handle each stock separately
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
col_rename = "Ra")
Ra## # A tibble: 132 × 3
## # Groups: symbol [4]
## symbol date Ra
## <chr> <date> <dbl>
## 1 JPM 2022-01-31 -0.0755
## 2 JPM 2022-02-28 -0.0458
## 3 JPM 2022-03-31 -0.0386
## 4 JPM 2022-04-29 -0.118
## 5 JPM 2022-05-31 0.108
## 6 JPM 2022-06-30 -0.148
## 7 JPM 2022-07-29 0.0335
## 8 JPM 2022-08-31 -0.0141
## 9 JPM 2022-09-30 -0.0812
## 10 JPM 2022-10-31 0.215
## # ℹ 122 more rowsRb <- c("^IXIC") %>%
tq_get(get = "stock.prices",
from = "2022-01-01") %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
col_rename = "Rb") # Name the column "Rb" for the baseline returns
Rb## # A tibble: 33 × 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 23 more rowsRaRb <- left_join(Ra, Rb, by = c("date" = "date"))
RaRb## # A tibble: 132 × 4
## # Groups: symbol [4]
## symbol date Ra Rb
## <chr> <date> <dbl> <dbl>
## 1 JPM 2022-01-31 -0.0755 -0.101
## 2 JPM 2022-02-28 -0.0458 -0.0343
## 3 JPM 2022-03-31 -0.0386 0.0341
## 4 JPM 2022-04-29 -0.118 -0.133
## 5 JPM 2022-05-31 0.108 -0.0205
## 6 JPM 2022-06-30 -0.148 -0.0871
## 7 JPM 2022-07-29 0.0335 0.123
## 8 JPM 2022-08-31 -0.0141 -0.0464
## 9 JPM 2022-09-30 -0.0812 -0.105
## 10 JPM 2022-10-31 0.215 0.0390
## # ℹ 122 more rowsRaRb_capm <- RaRb %>%
tq_performance(Ra = Ra, # This refers to the "Ra" column in RaRb (for stock returns)
Rb = Rb, # This refers to the "Rb" column in RaRb (for baseline returns)
performance_fun = table.CAPM)
RaRb_capm## # A tibble: 4 × 13
## # Groups: symbol [4]
## symbol ActivePremium Alpha AnnualizedAlpha Beta `Beta-` `Beta+` Correlation
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 JPM 0.082 0.009 0.114 0.718 1.20 0.0523 0.588
## 2 MS 0.0015 0.003 0.0367 0.740 0.535 0.894 0.575
## 3 DNB.OL 0.0549 0.0079 0.0994 0.249 0.669 0.135 0.305
## 4 NDA-FI… 0.0312 0.006 0.0749 0.366 0.321 0.567 0.395
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## # `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>RaRb_skewness <- RaRb %>%
tq_performance(Ra = Ra,
Rb = NULL,
performance_fun = skewness)
RaRb_skewness## # A tibble: 4 × 2
## # Groups: symbol [4]
## symbol skewness.1
## <chr> <dbl>
## 1 JPM 0.125
## 2 MS 0.216
## 3 DNB.OL -0.285
## 4 NDA-FI.HE 0.124JPM has a positive skewness of 0.124. MS has a positive skewness of 0.254. NDA-FI.HE has a positive skewness of 0.138. DNB.OL has a negative skewness of -0.279. Conclusion: The stocks MS (0.254), NDA-FI.HE (0.138), and JPM (0.124) all exhibit a positively skewed distribution of returns. Among these, MS has the highest positive skewness, indicating that it has the greatest potential for larger upside movements compared to the others.
DNB.OL is the only stock with a negatively skewed distribution, meaning it has a higher potential for larger downside movements.