Dance to the market beat : trading algorithmique et émergence rythmique

Overview & Synopsis

Abstract: This dissertation explores the intricate dynamics of information diffusion in high-frequency financial markets, where the transition from traditional floor trading to fully electronic platforms has dramatically reshaped the landscape. Our investigation is guided by three key research questions, each building upon the previous one to provide a comprehensive understanding of this evolving ecosystem. In Chapter 2, we embark on an empirical journey to uncover the rhythmic patterns of information dissemination in the stock market. Through a rigorous analysis of high-frequency trading data, we reveal a consistent pattern of information diffusion across all assets and several time periods. This pattern, characterized by an average of five events per cluster, resonates with similar rhythmic structures found in music and nature, suggesting a fundamental property of the market. We connect this observation to cognitive theories like Miller’s law and the El Farol bar problem, highlighting the potential for cognitive-like constraints in algorithmic interactions within financial markets. Chapter 3 probes the theoretical underpinnings of this observed market rhythm, exploring whether it arises from optimal information efficiency or operational constraints. By framing the market as an information channel and leveraging concepts from information theory and stochastic thermodynamics, we establish both a lower and upper bound on market reflexivity. The lower bound is dictated by the energetic costs of rapid information processing, while the upper bound is determined by the limits of information transmission capacity due to the noise introduced by algorithmic trading. We further propose a novel metric, Energy-Information Efficiency (EIE), to assess the balance between information processing and energetic costs in different stocks. We find that blue-chip stocks, often associated with high-frequency trading, are the most efficient under this metric. In Chapter 4, we turn our attention to the role of exchange heterogeneity in shaping information diffusion within fragmented markets. After calibrating it from empirical data, we demonstrate with an agent-based model that the diverse characteristics of exchanges, particularly their responsiveness, significantly influence market reflexivity. We find that highly reactive exchanges contribute to greater reflexivity, consistent with the optimal balance between channel capacity and energetic costs identified in Chapter 2. This highlights the importance of considering exchange speed when evaluating the impact of algorithmic trading on market efficiency. Overall, this dissertation offers a comprehensive exploration of information diffusion and price formation in high-frequency financial markets. Our findings contribute to the growing body of literature on market microstructure in the age of algorithmic trading, providing new insights into the complex interplay between market participants, technology, and information dynamics. The emergence of a consistent market rhythm, its theoretical underpinnings, and the role of infrastructure speed in shaping it, offer a novel perspective on the evolution and energetic impact of financial markets in the digital age.