Abstract
This study aims to analyze the development of global stock exchanges by integrating clustering, classification, and Shapley Values to identify growth patterns and understand the differences in market characteristics and dynamics. The research applies the K-means algorithm for clustering, which enables the segmentation of exchanges based on their similarities. This is followed by using the random forest algorithm to classify these clusters and evaluate the importance of various features. Shapley Values are employed to interpret the contribution of individual variables to the model's predictions, considering all possible combinations of features. The empirical analysis is based on data from 82 stock exchanges worldwide, sourced from organizations such as the World Federation of Exchanges and the International Monetary Fund. Key variables used include market capitalization, trading value, the number of listed companies, and share turnover velocity. The results highlight the significant heterogeneity among exchanges, with major markets like those in China and the United States forming distinct clusters due to their size, capitalization, and high trading activity. This distinction underscores their dominant position in the global financial landscape. Moreover, exchanges that have emerged from mergers, such as Euronext and NASDAQ Nordic, demonstrate superior characteristics compared to their peers, indicating that consolidation can be an effective strategy for competing with larger markets and enhancing global competitiveness. The study's findings show that integrating clustering, classification, and Shapley Values is a robust approach for uncovering complex structures within financial markets. This approach provides deeper insights for market participants and policymakers into the growth patterns and strategic positioning of stock exchanges, offering valuable implications for future market development and competition strategies.