library(tidyquant)
## Loading required package: lubridate
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
## Loading required package: PerformanceAnalytics
## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
##
## legend
## Loading required package: quantmod
## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
library(DT)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::first() masks xts::first()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::last() masks xts::last()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# Load necessary libraries
library(tidyquant)
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME')
stocks.prices <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
# Change column values
stocks.prices$symbol[stocks.prices$symbol == 'IMOEX.ME'] <- 'MOEX' # Replace 'MOEX' with the appropriate name
# Add more lines if you have other symbols to rename
# Print the first few rows of the modified data
head(stocks.prices)
## # A tibble: 6 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 MOEX 2022-01-31 3524. 3566. 3519. 3530. 0 3530.
## 2 MOEX 2022-02-01 3565. 3585. 3531. 3548. 0 3548.
## 3 MOEX 2022-02-02 3575. 3580. 3516. 3544. 0 3544.
## 4 MOEX 2022-02-03 3499. 3519. 3447. 3471. 0 3471.
## 5 MOEX 2022-02-04 3499. 3556. 3464. 3471. 0 3471.
## 6 MOEX 2022-02-07 3511. 3521. 3438. 3471. 0 3471.
# Load necessary libraries
library(tidyquant)
library(ggplot2)
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME')
stocks.prices <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
# Change column values
stocks.prices$symbol[stocks.prices$symbol == 'IMOEX.ME'] <- 'MOEX' # Replace 'MOEX' with the appropriate name
# Add more lines if you have other symbols to rename
# Visualize
stocks.prices %>%
ggplot(aes(x = date, y = adjusted)) +
geom_line(size = 2, color = 'steelblue') +
theme_minimal() +
facet_wrap(~symbol, scales = 'free_y', nrow = 2) +
labs(x = 'Date', y = 'Price') +
ggtitle('Price Chart for KOSPI and SKhynix')
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Load necessary libraries
library(tidyquant)
library(dplyr) # Add this line to load the dplyr library
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME')
stocks.prices <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
# Change column values
stocks.prices$symbol[stocks.prices$symbol == 'IMOEX.ME'] <- 'MOEX' # Replace 'MOEX' with the appropriate name
# Add more lines if you have other symbols to rename
# Calculate daily return
multpl_stock_daily_ret <- stocks.prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = 'daily',
col_rename = 'returns')
# Print the first few rows of the calculated daily returns
head(multpl_stock_daily_ret)
## # A tibble: 6 × 3
## # Groups: symbol [1]
## symbol date returns
## <chr> <date> <dbl>
## 1 MOEX 2022-01-31 0
## 2 MOEX 2022-02-01 0.00504
## 3 MOEX 2022-02-02 -0.00123
## 4 MOEX 2022-02-03 -0.0205
## 5 MOEX 2022-02-04 -0.0000490
## 6 MOEX 2022-02-07 -0.0000663
# Load necessary libraries
library(tidyquant)
library(dplyr)
library(ggplot2)
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME')
stocks.prices <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
# Change column values
stocks.prices$symbol[stocks.prices$symbol == 'IMOEX.ME'] <- 'MOEX' # Replace 'MOEX' with the appropriate name
# Add more lines if you have other symbols to rename
# Calculate daily return
multpl_stock_daily_ret <- stocks.prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = 'daily',
col_rename = 'returns')
# Visualize Daily Return
multpl_stock_daily_ret %>%
ggplot(aes(x = date, y = returns, color = symbol)) +
geom_bar(stat = 'identity', position = 'dodge', width = 0.5, size = 2) +
scale_y_continuous(labels = scales::percent) +
labs(x = 'Year', y = 'Daily Returns') +
theme_minimal() +
scale_color_brewer(palette = 'Set2') +
facet_wrap(~symbol, nrow = 2) +
ggtitle('Daily Return of MOEX') # Update the title accordingly
# Install and load the DT package
install.packages("DT")
## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)
library(DT)
# Load necessary libraries
library(tidyquant)
library(dplyr)
library(tidyr)
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME')
stocks.prices <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
# Change column values
stocks.prices$symbol[stocks.prices$symbol == 'IMOEX.ME'] <- 'MOEX' # Replace 'MOEX' with the appropriate name
# Add more lines if you have other symbols to rename
# Calculate daily return
multpl_stock_daily_ret <- stocks.prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = 'daily',
col_rename = 'returns')
# Spread the data for regression
Data <- multpl_stock_daily_ret %>%
spread(symbol, value = returns)
# Print the spread data using DT::datatable
DT::datatable(Data)
# Load necessary libraries
library(tidyquant)
library(dplyr)
library(tidyr)
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME')
stocks.prices <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
# Change column values
stocks.prices$symbol[stocks.prices$symbol == 'IMOEX.ME'] <- 'MOEX' # Replace 'MOEX' with the appropriate name
# Add more lines if you have other symbols to rename
# Calculate daily return
multpl_stock_daily_ret <- stocks.prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = 'daily',
col_rename = 'returns')
# Spread the data for regression
Data <- multpl_stock_daily_ret %>%
spread(symbol, value = returns)
# Applying one-factor model
reg <- lm(data = Data, MOEX ~ MOEX) # Adjust variable names accordingly
## Warning in model.matrix.default(mt, mf, contrasts): the response appeared on
## the right-hand side and was dropped
## Warning in model.matrix.default(mt, mf, contrasts): problem with term 1 in
## model.matrix: no columns are assigned
summary(reg)
##
## Call:
## lm(formula = MOEX ~ MOEX, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034697 -0.017028 0.002367 0.011341 0.036572
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002416 0.005602 -0.431 0.673
##
## Residual standard error: 0.0217 on 14 degrees of freedom
# Load necessary libraries
library(tidyquant)
library(dplyr)
library(tidyr)
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME')
stocks.prices <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
# Change column values
stocks.prices$symbol[stocks.prices$symbol == 'IMOEX.ME'] <- 'MOEX' # Replace 'MOEX' with the appropriate name
# Add more lines if you have other symbols to rename
# Calculate daily return
multpl_stock_daily_ret <- stocks.prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = 'daily',
col_rename = 'returns')
# Spread the data for regression
Data2 <- multpl_stock_daily_ret %>%
spread(symbol, value = returns)
# Calculate Expected Return
Data2$Expected_ret <- 1.2250926 * Data2$MOEX
# Calculate Abnormal Return
Data2$Abnormal_ret <- Data2$MOEX - Data2$Expected_ret # Adjust variable names accordingly
# Calculate t-statistic
Data2$t_stat <- Data2$Abnormal_ret / 0.0126 # Adjust the denominator accordingly
# Print the modified data
head(Data2)
## # A tibble: 6 × 5
## date MOEX Expected_ret Abnormal_ret t_stat
## <date> <dbl> <dbl> <dbl> <dbl>
## 1 2022-01-31 0 0 0 0
## 2 2022-02-01 0.00504 0.00617 -0.00113 -0.0900
## 3 2022-02-02 -0.00123 -0.00151 0.000277 0.0220
## 4 2022-02-03 -0.0205 -0.0251 0.00461 0.366
## 5 2022-02-04 -0.0000490 -0.0000600 0.0000110 0.000875
## 6 2022-02-07 -0.0000663 -0.0000812 0.0000149 0.00118
# Load necessary libraries
library(tidyquant)
library(dplyr)
library(tidyr)
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME')
stocks.prices <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
# Change column values
stocks.prices$symbol[stocks.prices$symbol == 'IMOEX.ME'] <- 'MOEX' # Replace 'MOEX' with the appropriate name
# Add more lines if you have other symbols to rename
# Calculate daily return
multpl_stock_daily_ret <- stocks.prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = 'daily',
col_rename = 'returns')
# Spread the data for regression
Data2 <- multpl_stock_daily_ret %>%
spread(symbol, value = returns)
# Calculate Expected Return
Data2$Expected_ret <- 1.2250926 * Data2$MOEX
# Calculate Abnormal Return
Data2$Abnormal_ret <- Data2$MOEX - Data2$Expected_ret # Adjust variable names accordingly
# Calculate Cumulative Abnormal Return (CAR)
Data2$CAR <- cumprod(1 + Data2$Abnormal_ret)
# Print the modified data
head(Data2)
## # A tibble: 6 × 5
## date MOEX Expected_ret Abnormal_ret CAR
## <date> <dbl> <dbl> <dbl> <dbl>
## 1 2022-01-31 0 0 0 1
## 2 2022-02-01 0.00504 0.00617 -0.00113 0.999
## 3 2022-02-02 -0.00123 -0.00151 0.000277 0.999
## 4 2022-02-03 -0.0205 -0.0251 0.00461 1.00
## 5 2022-02-04 -0.0000490 -0.0000600 0.0000110 1.00
## 6 2022-02-07 -0.0000663 -0.0000812 0.0000149 1.00
# Load necessary libraries
library(tidyquant)
library(dplyr)
library(tidyr)
library(ggplot2)
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME')
stocks.prices <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
# Change column values
stocks.prices$symbol[stocks.prices$symbol == 'IMOEX.ME'] <- 'MOEX' # Replace 'MOEX' with the appropriate name
# Add more lines if you have other symbols to rename
# Calculate daily return
multpl_stock_daily_ret <- stocks.prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = 'daily',
col_rename = 'returns')
# Spread the data for regression
Data2 <- multpl_stock_daily_ret %>%
spread(symbol, value = returns)
# Calculate Expected Return
Data2$Expected_ret <- 1.2250926 * Data2$MOEX
# Calculate Abnormal Return
Data2$Abnormal_ret <- Data2$MOEX - Data2$Expected_ret # Adjust variable names accordingly
# Calculate Cumulative Abnormal Return (CAR)
Data2$CAR <- cumprod(1 + Data2$Abnormal_ret)
# Visualize Cumulative Abnormal Return (CAR)
Data2 %>%
ggplot(aes(x = date, y = CAR)) +
geom_point(size = 5, color = 'steelblue') +
geom_point(aes(y = 1.0141802, x = as.Date('2022-07-27')), size = 5, color = 'red') +
annotate('text', x = as.Date('2022-07-30'), y = 1.0141802, label = 'Earnings Release', size = 4) +
theme_minimal() +
labs(x = 'Date', y = 'CAR') +
ggtitle('Cumulative Abnormal Return')
# Load necessary libraries
library(tidyquant)
library(ggplot2)
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME', 'Symbol2', 'Symbol3') # Replace 'Symbol2' and 'Symbol3' with the appropriate symbols
stocks.prices1 <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
## Warning: There were 2 warnings in `dplyr::mutate()`.
## The first warning was:
## ℹ In argument: `data.. = purrr::map(...)`.
## Caused by warning:
## ! x = 'Symbol2', get = 'stock.prices': Error in getSymbols.yahoo(Symbols = "Symbol2", env = <environment>, verbose = FALSE, : Unable to import "Symbol2".
## HTTP error 404.
## Removing Symbol2.
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
# Change column values if needed
# stocks.prices1$symbol[stocks.prices1$symbol == 'IMOEX.ME'] <- 'NewName' # Replace 'NewName' with the appropriate name
# Add more lines if you have other symbols to rename
# Visualize Price Chart for KOSPI, SKhynix, and Industry
stocks.prices1 %>%
ggplot(aes(x = date, y = adjusted)) +
geom_line(size = 2, color = 'steelblue') +
theme_minimal() +
facet_wrap(~symbol, scales = 'free_y', nrow = 3) +
labs(x = 'Date', y = 'Price') +
ggtitle('Price Chart for KOSPI, SKhynix, and Industry')
# Load necessary libraries
library(tidyquant)
library(dplyr)
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME', 'Symbol2', 'Symbol3') # Replace 'Symbol2' and 'Symbol3' with the appropriate symbols
stocks.prices1 <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
## Warning: There were 2 warnings in `dplyr::mutate()`.
## The first warning was:
## ℹ In argument: `data.. = purrr::map(...)`.
## Caused by warning:
## ! x = 'Symbol2', get = 'stock.prices': Error in getSymbols.yahoo(Symbols = "Symbol2", env = <environment>, verbose = FALSE, : Unable to import "Symbol2".
## HTTP error 404.
## Removing Symbol2.
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
# Change column values if needed
# stocks.prices1$symbol[stocks.prices1$symbol == 'IMOEX.ME'] <- 'NewName' # Replace 'NewName' with the appropriate name
# Add more lines if you have other symbols to rename
# Calculate daily return
multpl_stock_daily_ret1 <- stocks.prices1 %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = 'daily',
col_rename = 'returns')
# Print the first few rows of the calculated daily returns
head(multpl_stock_daily_ret1)
## # A tibble: 6 × 3
## # Groups: symbol [1]
## symbol date returns
## <chr> <date> <dbl>
## 1 IMOEX.ME 2022-01-31 0
## 2 IMOEX.ME 2022-02-01 0.00504
## 3 IMOEX.ME 2022-02-02 -0.00123
## 4 IMOEX.ME 2022-02-03 -0.0205
## 5 IMOEX.ME 2022-02-04 -0.0000490
## 6 IMOEX.ME 2022-02-07 -0.0000663
# Load necessary libraries
library(tidyquant)
library(dplyr)
library(ggplot2)
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME', 'Symbol2', 'Symbol3') # Replace 'Symbol2' and 'Symbol3' with the appropriate symbols
stocks.prices1 <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
## Warning: There were 2 warnings in `dplyr::mutate()`.
## The first warning was:
## ℹ In argument: `data.. = purrr::map(...)`.
## Caused by warning:
## ! x = 'Symbol2', get = 'stock.prices': Error in getSymbols.yahoo(Symbols = "Symbol2", env = <environment>, verbose = FALSE, : Unable to import "Symbol2".
## HTTP error 404.
## Removing Symbol2.
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
# Change column values if needed
# stocks.prices1$symbol[stocks.prices1$symbol == 'IMOEX.ME'] <- 'NewName' # Replace 'NewName' with the appropriate name
# Add more lines if you have other symbols to rename
# Calculate daily return
multpl_stock_daily_ret1 <- stocks.prices1 %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = 'daily',
col_rename = 'returns')
# Visualize Daily Return
multpl_stock_daily_ret1 %>%
ggplot(aes(x = date, y = returns, color = symbol)) +
geom_bar(stat = 'identity', position = 'dodge', width = 0.5, size = 2) +
scale_y_continuous(labels = scales::percent) +
labs(x = 'Year', y = 'Daily Returns') +
theme_minimal() +
scale_color_brewer(palette = 'Set2') +
facet_wrap(~symbol, nrow = 3) +
ggtitle('Daily Return of KOSPI, SKhynix, and Industry')
# Load necessary libraries
library(tidyquant)
library(dplyr)
library(tidyr)
library(DT)
# Import IMOEX.ME.csv data
symbols <- c('IMOEX.ME', 'Symbol2', 'Symbol3') # Replace 'Symbol2' and 'Symbol3' with the appropriate symbols
stocks.prices1 <- tq_get(symbols, get = 'stock.prices',
from = '2022-01-31',
to = '2022-02-20')
## Warning: There were 2 warnings in `dplyr::mutate()`.
## The first warning was:
## ℹ In argument: `data.. = purrr::map(...)`.
## Caused by warning:
## ! x = 'Symbol2', get = 'stock.prices': Error in getSymbols.yahoo(Symbols = "Symbol2", env = <environment>, verbose = FALSE, : Unable to import "Symbol2".
## HTTP error 404.
## Removing Symbol2.
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
# Change column values if needed
# stocks.prices1$symbol[stocks.prices1$symbol == 'IMOEX.ME'] <- 'NewName' # Replace 'NewName' with the appropriate name
# Add more lines if you have other symbols to rename
# Calculate daily return
multpl_stock_daily_ret1 <- stocks.prices1 %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = 'daily',
col_rename = 'returns')
# Convert returns to numeric
multpl_stock_daily_ret1$returns <- as.numeric(as.character(multpl_stock_daily_ret1$returns))
# Create Data3
Data3 <- multpl_stock_daily_ret1 %>%
spread(symbol, value = returns) %>%
drop_na()
# Print the data table using DT::datatable
DT::datatable(Data3)