Behavioral and Structural Dynamics in Cryptocurrency Markets

Abstract

Cryptocurrency markets provide a distinctive environment characterized by extreme swings and strong behavioral biases. Considering these features, this dissertation examines how behavioral factors and structural mechanisms, within their respective domains, influence cryptocurrency market dynamics across three essays. The first essay investigates whether investor attention promotes or mitigates daily herding behavior and examines how this effect varies with market sentiment. The results show that higher investor attention tends to reduce the likelihood of herding, suggesting that attention-driven trading facilitates information dissemination and enhances price efficiency. However, this mitigating effect diminishes under heightened fear, implying that attention-driven trading becomes less effective in fearful market conditions. The second essay examines the determinants of herding persistence by analyzing how herding episodes evolve over time. Using a discrete-time hazard model, the study investigates whether the duration of an episode affects the likelihood that herding continues. The results show that duration alone is insignificant; however, longer episodes tend to sustain herding persistence, indicating that herding behaviors do not dissipate quickly once established. Investor attention and sentiment interact with duration to counteract this persistence, suggesting that information-related factors play a key role in moderating prolonged collective behavior. Additionally, after the COIVD-19 shock, duration becomes negatively associated with persistence, indicating a structural change in how herding episodes unfold following a major market disruption. The third essay focuses on the state dynamics of cryptocurrencies, examining whether regime transitions depend on the states of neighboring cryptocurrencies. It defines bubble exposure and distress exposure as the proportions of neighboring cryptocurrencies in bubble and distress regimes, respectively, and decomposes the transition process into two components: occurrence and direction. This decomposition reveals that the mechanisms governing transition occurrence differ from those determining direction. The results show that only bubble exposure significantly affects transition occurrence, and its effect varies depending on the initial regime. In contrast, neither bubble nor distress exposure significantly influences transition direction. These findings suggest that while neighboring states can trigger regime shifts, their direction is primarily determined by coin-specific and market-wide factors. Collectively, these essays provide complementary perspectives on cryptocurrency markets, spanning behavioral dynamics and state transition processes. Together, they reveal that investor behavior and adaptive learning represent the behavioral dimension of market adjustment, whereas