Computational Methods for Analyzing Decentralized Finance Ecosystems

Decentralized Finance (DeFi) represents an emerging financial ecosystem that offers services such as lending, investing, and trading without traditional intermediaries like banks or financial institutions. Unlike conventional financial systems, users interact directly with software programs called smart contracts that encode financial logic and automate service delivery. This novel ecosystem promises transparency through public blockchain ledgers that make all transactions visible and inclusion through open access that eliminates traditional barriers to financial participation. Additionally, DeFi enables decentralized governance where users participate in protocol decision-making, and smart contracts facilitate advanced financial engineering through compositional service integration. However, despite these technical innovations, DeFi introduces significant challenges related to transaction complexity, governance concentration, and cybersecurity vulnerabilities that undermine its foundational promises. This thesis develops computational methods to systematically investigate these challenges in Decentralized Finance through empirical analysis of blockchain data. First, to address the complexity of DeFi compositions, we developed an algorithm that extracts fundamental building blocks from individual transactions, revealing recurring patterns and hidden interdependencies between financial services and assets that manual analysis cannot capture at scale. Second, we applied network analysis techniques and introduced novel measurements to examine the governance structures of decentralized applications, focusing on contributors with development and administrative roles. Our analysis revealed common voting patterns and centralized decision-making that contradict claims of decentralized governance. Third, we adapted a difference-in-differences statistical framework to quantify the economic impact of cybercrime on governance tokens, demonstrating that indirect effects on prices and trading volumes significantly exceed the direct losses suffered by immediate victims. These computational methods collectively provide the first systematic, large-scale analytical framework for empirically investigating DeFi ecosystems, revealing fundamental gaps between theoretical promises of transparency and inclusion and practical realities. The findings have significant implications for researchers, policymakers, and practitioners by establishing evidence-based approaches to measuring decentralization claims and systemic risks in blockchain-based financial systems.