Agentic Intelligence

AI Methods for Bond Markets
Erscheinungsjahr 2026
ISBN: 978-3-032-30595-4
Verlag: Springer

Buch, Englisch, 242 Seiten, Format (B × H): 155 mm x 235 mm

What does it mean for an AI system to be agentic? answers this through modeling one of modern finance’s hardest problems: Government bond market trading. As structural pressures and regulatory concerns mount, traditional approaches fail in these complex, partially observable and bilateral trading environments.  

Following unprecedented pandemic-era government debt issuance, authorities including the Reserve Bank of Australia, Bank of Canada, Bank of England, and Federal Reserve Bank of New York have prioritised enhancing the market’s liquidity and functioning. This book responds directly by building Agentic AI models – showing that Agent-Based Models (ABMs) provide the natural computational foundation for transparent market modelling, and that when LLMs are embedded with discipline, Agentic AI can uncover emergent features and behavioural realism that traditional models cannot. Dynamically adaptive and able to operate over a virtually infinite space of possibilities, Agentic AI offers a new paradigm.

The author makes three practical contributions: (1) a liquidity-centred framework that treats the ability to transact as the central systemic object; (2) Agentic AI methods that capture heterogeneous participant interaction, non-linear cost structures, and emergent liquidity; and (3) the AI-simulator testing paradigm, in which the simulator is the laboratory and the agent is the experiment – providing market participants and regulators a basis on which Agentic AI can be deployed, governed, and trusted. Readers will gain implementable methodologies for moving Agentic AI from concept to market application, supporting trustworthy AI in the trading, supervision and design of financial markets.

Frank Fabozzi, Professor of Practice at Johns Hopkins Carey Business School, Author of the best-selling

David Rensin, Distinguished Engineer; Technical Advisor to the CFO at Google

Dr. Belinda Middleweek, Senior Lecturer, University of Technology Sydney

Daniele Grassi, CEO & Co-Founder Axyon AI

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Vidler, Alicia

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik

Inhaltsverzeichnis

Dedication.- Foreword.- Preface.- Acknowledgements.- Declarations.- 1. Introduction.- 2. The New Frontier of AI in Finance: Bond Markets.- 3. Agent-Based Market Modelling.- 4. Humans and Machines: Agentic AI methods in Markets.- 5. Large Language Models as Agents: Using LLMs in Financial Decision Making.- 6. LLMs in Strategic Decision Making.- 7. Agentic AI: Making It All Real.- 8. An Agentic Market Model for OTC Trading: TRIBE(LLM).- 9. Conclusion.- Appendix A. SugarScape ABM Model of the Australian Bond Market.- Appendix B. Complete experimental prompts.- Appendix C. LLM Runaway Conversations and Model Comparisons.- Appendix D. TRIBE(LLM) Detailed Pseudocode.

Über Autor(innen)

Alicia Vidler is a senior researcher and financial markets expert working at the intersection of artificial intelligence and capital markets. With extensive experience in the finance sector, she co-founded and led an AI-driven hedge fund and held senior roles at global financial institutions including Merrill Lynch and Bank of America. She completed her doctorate at the University of New South Wales, Australia, under the supervision of renowned AI expert Professor Toby Walsh, focusing on the application of Agentic AI - agent-based modeling and large language models to bond markets. As a Visiting Academic at Bar Ilan University in Tel Aviv, she continues to expand her research internationally. Alicia is also a passionate advocate for gender diversity in finance, serving on the boards of several NGOs in this space. Her unique combination of industry experience and academic research positions her at the forefront of financial AI innovation.