Equity Market Structure and Trading Diversification: Insights from Panel Data, Clustering, and Machine Learning

This paper studies the topic that has been rather less explored until now—the internal diversification of trading. Unlike looking at aggregate measures of financial development such as market capitalization and liquidity, the study focuses on trading diversification, defined as the portion of trading volume attributed to firms other than the ten most actively traded (VTX). The empirical analysis is based on the World Bank’s Global Financial Development database. It covers an unbalanced cross-country dataset of 2004–2021. Due to limited data availability, the resulting database became smaller and has an unbalanced panel structure. Four main independent variables in the core regression specification are related to financial structure (bank deposits) and financial integration (remittances, international public debt), as well as external measures of financial development (market capitalization, excluding firms within VTX). A broad range of control variables are introduced into the model to account for macroeconomic conditions, financial development, market size, liquidity, and participation. Lagged regressors are introduced to address persistence, delays, and potential endogeneity issues. The methodology relies on panel data econometrics, hierarchical clustering, and machine learning. The findings show that market structure and remittances positively affect trading diversification, whereas banks’ dominance and international public debt contribute to its concentration. The results persist across alternative specifications and robustness tests. The country-level analysis shows a core–periphery pattern, while machine learning demonstrates the critical importance of market structure.