Analysis of Global Stock Market Development - Integration of Clustering, Classification, and Shapley Values

Files:- stock_exchanges_data.csv:This file provides data on key financial indicators for 82 global stock exchanges, including Market Capitalization, Capitalization-to-GDP Ratio, Value Traded, Value Traded to GDP Ratio, Share Turnover Velocity, Capitalization per Listed Company, and Number of Trades. The data reflects the year 2023 and serves as the foundation for clustering and classification analysis within the study, focusing on identifying development patterns and key factors influencing stock exchange stability and competitiveness.- research_code.ipynb:This Jupyter Notebook contains the complete Python code used for the analysis conducted in the study. It includes data preparation, clustering, classification, Shapley values calculation, and all other analytical steps described in the paper. The notebook is fully reproducible based on the provided dataset.Raw data (csv files). Source: The World Federation of Exchanges (WFE) and International Monetary Fund (IMF)

文件列表：
- stock_exchanges_data.csv：该文件包含全球82家证券交易所的核心财务指标数据，涵盖市值（Market Capitalization）、市值GDP占比（Capitalization-to-GDP Ratio）、交易金额、交易金额GDP占比（Value Traded to GDP Ratio）、股票换手率（Share Turnover Velocity）、单家上市公司平均市值（Capitalization per Listed Company）以及交易总笔数（Number of Trades）。本数据的统计周期为2023年，作为本研究中聚类与分类分析的基础数据集，用于挖掘证券交易所的发展模式，以及影响其稳定性与竞争力的关键因素。
- research_code.ipynb：该Jupyter Notebook包含本研究中所用的完整Python分析代码，涵盖数据预处理、聚类分析、分类建模、夏普利值（Shapley values）计算等论文中描述的全部分析步骤。基于本数据集即可完全复现该Notebook中的所有分析流程。
原始数据（CSV格式文件）来源：世界交易所联合会（World Federation of Exchanges, WFE）与国际货币基金组织（International Monetary Fund, IMF）