Taiwan Listed Companies Daily Prices
Derren Ramadhan Saputra
2026-10-05
Introduction
This report presents the daily stock price data of listed companies in the Taiwan stock market over the most recent two-year period.
The data were obtained from the Taiwan Economic Journal (TEJ) database using the TSE/OTC Adjusted Price (Daily) dataset.
The objective of this assignment is to download, organize, inspect, and summarize daily stock price data for listed companies in Taiwan using R and R Markdown.
Data Source
The dataset was obtained from the Taiwan Economic Journal (TEJ) database.
The selected securities include common stocks from the Taiwan Stock Exchange (TWSE) and GreTai/Taipei Exchange market.
The observation period covers approximately two years, from October 2024 to October 2026.
The variables used in this report are:
- Company ID
- Company Name
- Date
- Open Price
- High Price
- Low Price
- Close Price
- Trading Volume
Import Data
The combined TEJ dataset is imported from a CSV file.
stock_data <- read_csv(
"Taiwan_Stocks_2024_2026_FINAL.csv",
show_col_types = FALSE
)Data Preparation
The Date variable is converted into R’s date format.
stock_data <- stock_data %>%
mutate(
Date = as.Date(Date, format = "%Y/%m/%d"),
COID = as.character(COID)
)The structure of the dataset is shown below.
glimpse(stock_data)## Rows: 957,924
## Columns: 8
## $ COID <chr> "1101", "1101", "1101", "1101", "1101", "1101", "1101"…
## $ COID_Name <chr> "TCC", "TCC", "TCC", "TCC", "TCC", "TCC", "TCC", "TCC"…
## $ Date <date> 2024-10-07, 2024-10-08, 2024-10-09, 2024-10-11, 2024-…
## $ `Open(NTD)` <dbl> 31.4261, 30.9148, 30.5893, 30.4499, 30.2639, 30.3104, …
## $ `High(NTD)` <dbl> 31.4726, 31.1007, 30.6358, 30.6823, 30.4034, 30.4034, …
## $ `Low(NTD)` <dbl> 30.9612, 30.5893, 30.1709, 30.1245, 30.1709, 30.1709, …
## $ `Close(NTD)` <dbl> 31.1472, 30.7288, 30.1709, 30.2174, 30.2639, 30.2174, …
## $ `Volume(1000S)` <dbl> 13191, 14236, 11396, 9445, 6059, 8102, 10435, 7485, 10…Data Preview
The first 10 observations are displayed below.
head(stock_data, 10)## # A tibble: 10 × 8
## COID COID_Name Date `Open(NTD)` `High(NTD)` `Low(NTD)` `Close(NTD)`
## <chr> <chr> <date> <dbl> <dbl> <dbl> <dbl>
## 1 1101 TCC 2024-10-07 31.4 31.5 31.0 31.1
## 2 1101 TCC 2024-10-08 30.9 31.1 30.6 30.7
## 3 1101 TCC 2024-10-09 30.6 30.6 30.2 30.2
## 4 1101 TCC 2024-10-11 30.4 30.7 30.1 30.2
## 5 1101 TCC 2024-10-14 30.3 30.4 30.2 30.3
## 6 1101 TCC 2024-10-15 30.3 30.4 30.2 30.2
## 7 1101 TCC 2024-10-16 30.2 30.2 29.9 29.9
## 8 1101 TCC 2024-10-17 29.9 30.3 29.9 30.2
## 9 1101 TCC 2024-10-18 30.3 30.4 29.9 30.4
## 10 1101 TCC 2024-10-21 30.6 30.6 30.0 30.0
## # ℹ 1 more variable: `Volume(1000S)` <dbl>Dataset Dimensions
The number of observations and variables in the dataset is:
dim(stock_data)## [1] 957924 8The dataset contains 957,924 daily company observations and 8 variables.
Number of Listed Companies
The number of unique companies included in the dataset is calculated below.
stock_data %>%
summarise(
Number_of_Companies = n_distinct(COID)
)## # A tibble: 1 × 1
## Number_of_Companies
## <int>
## 1 1987The dataset contains 1,987 unique company IDs.
Date Range
The earliest and latest trading dates in the dataset are:
stock_data %>%
summarise(
Start_Date = min(Date),
End_Date = max(Date),
Number_of_Trading_Days = n_distinct(Date)
)## # A tibble: 1 × 3
## Start_Date End_Date Number_of_Trading_Days
## <date> <date> <int>
## 1 2024-10-07 2026-10-05 485The available data run from October 7, 2024 to October 5, 2026, covering 485 trading days.
Although the requested period begins around October 5, 2024, stock market data are only available on trading days. Therefore, the first available observation is October 7, 2024.
Missing Value Check
The dataset is checked for missing observations.
colSums(is.na(stock_data))## COID COID_Name Date Open(NTD) High(NTD)
## 0 0 0 0 0
## Low(NTD) Close(NTD) Volume(1000S)
## 0 0 0The selected variables do not contain missing values.
Duplicate Check
Each company should have no more than one observation for the same trading date.
duplicate_check <- stock_data %>%
count(COID, Date) %>%
filter(n > 1)
nrow(duplicate_check)## [1] 0A result of 0 indicates that there are no duplicated company-date observations.
Descriptive Statistics
Summary statistics for the price and trading volume variables are presented below.
stock_data %>%
summarise(
Mean_Open = mean(`Open(NTD)`, na.rm = TRUE),
Mean_High = mean(`High(NTD)`, na.rm = TRUE),
Mean_Low = mean(`Low(NTD)`, na.rm = TRUE),
Mean_Close = mean(`Close(NTD)`, na.rm = TRUE),
Mean_Volume = mean(`Volume(1000S)`, na.rm = TRUE)
)## # A tibble: 1 × 5
## Mean_Open Mean_High Mean_Low Mean_Close Mean_Volume
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 108. 111. 106. 108. 2893.Number of Companies by Trading Day
The following calculation shows how many companies have observations on each trading day.
daily_company_count <- stock_data %>%
group_by(Date) %>%
summarise(
Number_of_Companies = n_distinct(COID),
.groups = "drop"
)
head(daily_company_count)## # A tibble: 6 × 2
## Date Number_of_Companies
## <date> <int>
## 1 2024-10-07 1935
## 2 2024-10-08 1935
## 3 2024-10-09 1935
## 4 2024-10-11 1935
## 5 2024-10-14 1935
## 6 2024-10-15 1936Visualization
The figure below shows the number of companies with available observations on each trading day.
ggplot(
daily_company_count,
aes(x = Date, y = Number_of_Companies)
) +
geom_line() +
labs(
title = "Number of Taiwan Listed Companies with Daily Price Data",
x = "Trading Date",
y = "Number of Companies"
) +
theme_minimal()
Sample Company
As an example, the following table displays the first observations for company code 2330.
stock_data %>%
filter(COID == "2330") %>%
select(
COID,
COID_Name,
Date,
`Open(NTD)`,
`High(NTD)`,
`Low(NTD)`,
`Close(NTD)`,
`Volume(1000S)`
) %>%
head(10)## # A tibble: 10 × 8
## COID COID_Name Date `Open(NTD)` `High(NTD)` `Low(NTD)` `Close(NTD)`
## <chr> <chr> <date> <dbl> <dbl> <dbl> <dbl>
## 1 2330 TSMC 2024-10-07 965. 981. 961. 976.
## 2 2330 TSMC 2024-10-08 971. 981. 969. 981.
## 3 2330 TSMC 2024-10-09 1001. 1005. 991. 991.
## 4 2330 TSMC 2024-10-11 996. 1020. 991. 1015.
## 5 2330 TSMC 2024-10-14 1015. 1025. 1005. 1015.
## 6 2330 TSMC 2024-10-15 1020. 1044. 1020. 1039.
## 7 2330 TSMC 2024-10-16 1010. 1039. 1005. 1015.
## 8 2330 TSMC 2024-10-17 1020. 1025. 1001. 1005.
## 9 2330 TSMC 2024-10-18 1064. 1069. 1044. 1054.
## 10 2330 TSMC 2024-10-21 1059. 1064. 1049. 1054.
## # ℹ 1 more variable: `Volume(1000S)` <dbl>Conclusion
This report successfully imported and organized daily stock price data for companies listed in the Taiwan stock market over the most recent two-year period.
The final dataset contains 957,924 observations, 1,987 unique company IDs, and 485 trading days from October 7, 2024 to October 5, 2026.
The dataset includes daily open, high, low, and close prices together with trading volume. Data validation also shows that there are no missing values in the selected variables and no duplicated company-date observations.
The data are therefore ready for additional financial or time-series analysis in R.