title: “Taiwan Stock Market Daily Prices” author: “Delgermaa” date: “r Sys.Date()” output: html_document: toc: true toc_float: true knitr::opts_chunk$set( echo = TRUE, warning = FALSE, message = FALSE —
title: “Taiwan Stock Market Daily Prices” author: “Delgermaa” date: “2026-10-06” output: html_document: toc: true toc_float: true —————
1. Import Data
stock_data <- read.csv(
"hw3.csv",
stringsAsFactors = FALSE
)
head(stock_data)## CO_ID Date Open High Low Close
## 1 0053 PTE 10/4/2024 95.4244 95.4244 94.4654 94.4654
## 2 0050 Yuanta Taiwan Top50 10/4/2024 44.1573 44.4692 43.9173 44.0613
## 3 0056 PTD 10/4/2024 31.0855 31.1824 30.8513 30.9644
## 4 0051 TMT 10/4/2024 76.4016 76.4016 74.9954 75.2767
## 5 0057 FB MSCI Taiwan 10/4/2024 136.2000 136.9500 135.7000 135.7000
## 6 0052 FB Technology 10/4/2024 24.5660 24.7366 24.3681 24.4636str(stock_data)## 'data.frame': 2838 obs. of 6 variables:
## $ CO_ID: chr "0053 PTE" "0050 Yuanta Taiwan Top50" "0056 PTD" "0051 TMT" ...
## $ Date : chr "10/4/2024" "10/4/2024" "10/4/2024" "10/4/2024" ...
## $ Open : num 95.4 44.2 31.1 76.4 136.2 ...
## $ High : num 95.4 44.5 31.2 76.4 136.9 ...
## $ Low : num 94.5 43.9 30.9 75 135.7 ...
## $ Close: num 94.5 44.1 31 75.3 135.7 ...2. Data Preparation
# Convert Date to Date format
stock_data$Date <- as.Date(
stock_data$Date,
format = "%m/%d/%Y"
)
# Convert price columns to numeric
stock_data$Open <- as.numeric(stock_data$Open)
stock_data$High <- as.numeric(stock_data$High)
stock_data$Low <- as.numeric(stock_data$Low)
stock_data$Close <- as.numeric(stock_data$Close)
# Remove rows with missing important values
stock_data <- stock_data %>%
filter(
!is.na(CO_ID),
!is.na(Date)
)
str(stock_data)## 'data.frame': 2838 obs. of 6 variables:
## $ CO_ID: chr "0053 PTE" "0050 Yuanta Taiwan Top50" "0056 PTD" "0051 TMT" ...
## $ Date : Date, format: "2024-10-04" "2024-10-04" ...
## $ Open : num 95.4 44.2 31.1 76.4 136.2 ...
## $ High : num 95.4 44.5 31.2 76.4 136.9 ...
## $ Low : num 94.5 43.9 30.9 75 135.7 ...
## $ Close: num 94.5 44.1 31 75.3 135.7 ...3. Data Overview
# Number of rows and columns
dim(stock_data)## [1] 2838 6# Column names
names(stock_data)## [1] "CO_ID" "Date" "Open" "High" "Low" "Close"# First date
min(stock_data$Date, na.rm = TRUE)## [1] "2024-10-04"# Last date
max(stock_data$Date, na.rm = TRUE)## [1] "2024-10-04"# Number of unique companies
length(unique(stock_data$CO_ID))## [1] 28384. Company Summary
company_summary <- stock_data %>%
group_by(CO_ID) %>%
summarise(
Observations = n(),
First_Date = min(Date, na.rm = TRUE),
Last_Date = max(Date, na.rm = TRUE),
Average_Close = mean(Close, na.rm = TRUE),
Min_Close = min(Close, na.rm = TRUE),
Max_Close = max(Close, na.rm = TRUE),
.groups = "drop"
)
head(company_summary, 20)## # A tibble: 20 × 7
## CO_ID Observations First_Date Last_Date Average_Close Min_Close Max_Close
## <chr> <int> <date> <date> <dbl> <dbl> <dbl>
## 1 0050 Yu… 1 2024-10-04 2024-10-04 44.1 44.1 44.1
## 2 0051 TMT 1 2024-10-04 2024-10-04 75.3 75.3 75.3
## 3 0052 FB… 1 2024-10-04 2024-10-04 24.5 24.5 24.5
## 4 0053 PTE 1 2024-10-04 2024-10-04 94.5 94.5 94.5
## 5 0055 PTF 1 2024-10-04 2024-10-04 26.7 26.7 26.7
## 6 0056 PTD 1 2024-10-04 2024-10-04 31.0 31.0 31.0
## 7 0057 FB… 1 2024-10-04 2024-10-04 136. 136. 136.
## 8 0061 PW… 1 2024-10-04 2024-10-04 24.5 24.5 24.5
## 9 006201 … 1 2024-10-04 2024-10-04 22.7 22.7 22.7
## 10 006203 … 1 2024-10-04 2024-10-04 83.2 83.2 83.2
## 11 006204 … 1 2024-10-04 2024-10-04 109. 109. 109.
## 12 006205 … 1 2024-10-04 2024-10-04 38.1 38.1 38.1
## 13 006206 … 1 2024-10-04 2024-10-04 37.0 37.0 37.0
## 14 006207 … 1 2024-10-04 2024-10-04 29 29 29
## 15 006208 … 1 2024-10-04 2024-10-04 101. 101. 101.
## 16 00625K … 1 2024-10-04 2024-10-04 7.62 7.62 7.62
## 17 00631L … 1 2024-10-04 2024-10-04 10.4 10.4 10.4
## 18 00632R … 1 2024-10-04 2024-10-04 23.9 23.9 23.9
## 19 00633L … 1 2024-10-04 2024-10-04 53.5 53.5 53.5
## 20 00634R … 1 2024-10-04 2024-10-04 3.63 3.63 3.635. Descriptive Statistics
summary(
stock_data[, c("Open", "High", "Low", "Close")]
)## Open High Low Close
## Min. : 1.46 Min. : 1.46 Min. : 1.46 Min. : 1.46
## 1st Qu.: 24.89 1st Qu.: 25.15 1st Qu.: 24.55 1st Qu.: 24.85
## Median : 43.82 Median : 44.30 Median : 42.98 Median : 43.42
## Mean : 85.64 Mean : 86.63 Mean : 84.24 Mean : 85.18
## 3rd Qu.: 99.42 3rd Qu.: 99.61 3rd Qu.: 98.10 3rd Qu.: 98.74
## Max. :10121.60 Max. :10313.70 Max. :10254.50 Max. :10293.106. Daily Closing Price Visualization
# Select the first five companies
selected_companies <- unique(stock_data$CO_ID)[1:5]
sample_data <- stock_data %>%
filter(
CO_ID %in% selected_companies
)
ggplot(
sample_data,
aes(
x = Date,
y = Close,
color = as.factor(CO_ID)
)
) +
geom_line(linewidth = 0.7) +
labs(
title = "Daily Closing Prices of Selected Taiwan Stocks",
x = "Date",
y = "Closing Price",
color = "Stock"
) +
theme_minimal()
7. Daily Returns
stock_return <- stock_data %>%
arrange(CO_ID, Date) %>%
group_by(CO_ID) %>%
mutate(
Daily_Return = Close / lag(Close) - 1
) %>%
ungroup()
head(stock_return)## # A tibble: 6 × 7
## CO_ID Date Open High Low Close Daily_Return
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0050 Yuanta Taiwan Top50 2024-10-04 44.2 44.5 43.9 44.1 NA
## 2 0051 TMT 2024-10-04 76.4 76.4 75.0 75.3 NA
## 3 0052 FB Technology 2024-10-04 24.6 24.7 24.4 24.5 NA
## 4 0053 PTE 2024-10-04 95.4 95.4 94.5 94.5 NA
## 5 0055 PTF 2024-10-04 26.8 26.9 26.7 26.7 NA
## 6 0056 PTD 2024-10-04 31.1 31.2 30.9 31.0 NAReturn Summary
return_summary <- stock_return %>%
group_by(CO_ID) %>%
summarise(
Average_Return = mean(
Daily_Return,
na.rm = TRUE
),
Volatility = sd(
Daily_Return,
na.rm = TRUE
),
.groups = "drop"
)
head(return_summary, 20)## # A tibble: 20 × 3
## CO_ID Average_Return Volatility
## <chr> <dbl> <dbl>
## 1 0050 Yuanta Taiwan Top50 NaN NA
## 2 0051 TMT NaN NA
## 3 0052 FB Technology NaN NA
## 4 0053 PTE NaN NA
## 5 0055 PTF NaN NA
## 6 0056 PTD NaN NA
## 7 0057 FB MSCI Taiwan NaN NA
## 8 0061 PWC300 NaN NA
## 9 006201 Yuanta TW TPEx 50 NaN NA
## 10 006203 Yuanta MSCI TW ETF NaN NA
## 11 006204 SinoPac TAIEX ETF NaN NA
## 12 006205 FB SSE180 NaN NA
## 13 006206 YT SSE50 NaN NA
## 14 006207 Fuh Hwa CSI 300 NaN NA
## 15 006208 FB TW50 NaN NA
## 16 00625K FB SSE180+R NaN NA
## 17 00631L T50Bull2X NaN NA
## 18 00632R T50Bear1X NaN NA
## 19 00633L Fubon SSE180 L2X ETF NaN NA
## 20 00634R FubonSSE180 INVR.ETF NaN NA8. Conclusion
This report analyzes daily stock prices of companies listed in the Taiwan stock market.
The dataset contains daily Open, High, Low, and Close prices for many listed companies. The data were imported, cleaned, and converted into appropriate formats in R.
Descriptive statistics were calculated for the stock prices, including the minimum, maximum, mean, and other summary measures. Daily closing price trends were also visualized for selected stocks.
In addition, daily returns and volatility were calculated to provide further information about stock price movements and the risk associated with individual stocks.
Overall, the analysis provides an overview of Taiwan stock market daily price movements and demonstrates how R can be used to clean, summarize, analyze, and visualize financial market data.