Background As the financial markets are becoming increasingly complicated, there is a need to understand the evolving market microstructure landscape. While the existing research is well-focused on quantitative aspects of financial markets, qualitative aspects remain underexplored. The purpose of this study is to explore the market microstructure of cryptocurrencies through a mixed-methodological approach. Methods By leveraging podcast analysis as primary data source, the study aims to uncover core thematic dimensions through techniques of lexicometry using Iramuteq. A total of 12 domain-specific podcasts are analyzed and processed in the study for descending hierarchical classification, resulting in five lexical clusters. These clusters are then subjected to factorial analysis using the Scikit-learn library in Python (v3.10). Results The descending hierarchical classification reveals five thematic clusters reflecting institutional infrastructure, market foundation, efficiency and resilience, forecasting and modelling dynamics, and market structure and efficiency. Followed by this, the factorial analysis discloses three dominant factors; market structure, forecasting models, and resilience mechanisms that shape the understanding of cryptocurrency market microstructure. The findings indicate that discussions on market microstructure of cryptocurrencies are multidimensional and are closely linked with both technological and regulatory developments. Conclusion The study contributes to literature by introducing a novel data source for qualitative finance research. The use of podcasts enhances the depth of extracted themes. Practitioners and academicians can gain updated insights on evolving market microstructure mechanisms for digital financial markets. The originality of this study lies in its unique approach, bridging top industry expert insights with quantitative techniques to offer a deeper understanding and future direction in market microstructure of cryptocurrencies.
Sharma et al. (2026) studied this question.