This R script conducts time series analysis and visualization of selected Nikkei 225 stocks. Utilizing quantmod for data retrieval and dplyr for data manipulation, it fetches one-year historical stock data from Yahoo Finance. The script calculates the most recent and prior year weekdays to ensure accurate trading data. It dynamically generates variables for each stock’s data and visualizes them using chartSeries with a clean theme. This streamlined process aids financial analysts and researchers in efficiently analyzing and visualizing stock performance.
Keywords
Time Series, Nikkei 225, Visualization
1 Time Series Analysis and Visualization of Nikkei 225 Stocks Using R
This script performs a detailed time series analysis and visualization of selected Nikkei 225 stocks using R programming language. The analysis begins by installing and loading the necessary packages, quantmod and dplyr. The quantmod package is essential for obtaining and analyzing financial data, while dplyr is used for efficient data manipulation.
The script then creates a list of Nikkei 225 stocks, including their codes and names, and generates a shortened version of the stock codes. A custom operator %ni% is defined to filter out specific stock codes that are not required for the analysis.
Current date and the most recent weekday are calculated using Sys.Date and weekdays functions. If the current date is a weekend, the script adjusts to the most recent preceding weekday. Similarly, the script calculates the date one year prior and adjusts for weekends to ensure the start date is also a weekday. This ensures that the data covers exactly one year of trading days, avoiding non-trading days which could skew the analysis.
A loop iterates over each stock in the filtered list, using the getSymbols function from the quantmod package to download historical stock data from Yahoo Finance. The downloaded data spans from the calculated start date to the most recent weekday. The assign function dynamically creates variables to store the downloaded data.
Each stock’s data is then visualized using the chartSeries function. This function generates time series charts with a specified theme, in this case, a white background, and omits additional technical analysis overlays. The charts are titled with the respective stock names for clarity.
This script provides a streamlined approach to retrieving, filtering, and visualizing financial data, making it useful for financial analysts, researchers, and investors interested in the historical performance of Nikkei 225 stocks. By automating the process of data retrieval and visualization, it enhances productivity and accuracy, reducing the manual effort required for similar tasks.
Moreover, the script’s structure allows for easy modification and expansion to include additional stocks or different indices. The use of R and its powerful libraries exemplifies the benefits of leveraging programming for financial data analysis, offering reproducibility, flexibility, and the ability to handle large datasets efficiently.
