Skip to content

Peer‐reviewed research using BSE

Jump to bottom Edit New page
Dave Cliff edited this page Mar 28, 2024 · 11 revisions

Here is a list of some peer-reviewed research papers, and doctoral theses, that used BSE as the platform for their market simulation experiments.

  1. N. Alexandrov, D. Cliff, & C. Figuero, (2021) Exploring Coevolutionary Dynamics of Competitive Arms-Races Between Infinitely Diverse Heterogenous Adaptive Automated Trader-Agents. Accepted for publication at the 16th Annual Social Simulation Conference, Krakow, Poland, 20--24 September 2021., Available at SSRN: https://ssrn.com/abstract=3901889

  2. H. Ashton (2022) Law breaking trading algorithms: Emergence and deterrence. PhD thesis, Department of Computer Science, University College London. Available from UCL: https://discovery.ucl.ac.uk/id/eprint/10155509/

  3. B. Benedicto, M. Madaleno a& A. Botelho (2023) Trading robots and financial markets trading solutions: the role of experimental economics. Journal of Investment Strategies, 12(1):45-67. Available from SSRN ($$$): https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4513847

  4. A. Bokhari & D. Cliff (2022) Studying Narrative Economics by Adding Continuous-Time Opinion Dynamics to an Agent-Based Model of Co-Evolutionary Adaptive Financial Markets. Proceedings of ICAART 2022. Available at SSRN: https://ssrn.com/abstract=4316574.

  5. A. Bokhari & D. Cliff (2023) Exploring Narrative Economics: An Agent-Based Co-Evolutionary Model Featuring Nonlinear Continuous-Time Opinion Dynamics In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence: Revised Selected Papers from ICAART 2023. Lecture Notes in Computer Science(), vol 14546. Springer. Available from Springer: https://link.springer.com/chapter/10.1007/978-3-031-55326-4_19

  6. A. Bokhari (2024) Exploring the Impact of Competing Narratives on Financial Markets I: An Opinionated Trader Agent-Based Model as a Practical Testbed. In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART2024). Available from Scitepress: https://www.scitepress.org/Papers/2024/124295/.

  7. A. Bokhari (2024) Exploring the Impact of Competing Narratives on Financial Markets II: An Opinionated Trader Agent-Based Model with Dynamic Feedback. In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART2024). Available from Scitepress: https://www.scitepress.org/Link.aspx?doi=10.5220/0012456500003636

  8. M. Borsi (2022) Imperfect Oracles: The Effect of Strategic Information on Stock Markets. In: Proceedings of ICAART 2021. Available on Arxiv: https://arxiv.org/pdf/2011.10837.pdf

  9. G. Church & D. Cliff (2019) A Simulator for Studying Automated Block Trading on a Coupled Dark/Lit Financial Exchange with Reputation Tracking. In M. Affenzeller, A. Bruzzone, F. Longo, & G. Pereira (Eds.), Proceedings of the 31st European Modelling and Simulation Symposium (EMSS2019) (pp. 284-293). DIME University of Genoa. Available from: University of Bristol Open Access.

  10. A. Cismaru (2023) DeepTraderX: Challenging Conventional Trading Strategies with Deep Learning in Multi-Threaded Market Simulations. In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART 2024) - Volume 3, pp.412-421. Avaialble from SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4692622

  11. D. Cliff & M. Rollins (2020) Methods Matter: A Trading Agent with No Intelligence Routinely Outperforms AI-Based Traders. In: Proceedings of the IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr2020). Available at Arxiv: https://arxiv.org/pdf/2011.14346.pdf

  12. D. Cliff (2022) Co-evolutionary Dynamics in a Simulation of Interacting Financial-Market Adaptive Automated Trading Systems. Proceedings of the 34th European Modeling and Simulation Symposium (EMSS2022). Available at SSRN: https://ssrn.com/abstract=4154426.

  13. D. Cliff (2022) Metapopulation Differential Co-Evolution of Trading Strategies in a Model Financial Market, in Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI2022), Singapore, December 2022. Preprint available here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4153519

  14. D. Cliff, Z. Zhang, & N. Taylor, (2022) Adding Supply/Demand Imbalance-Sensitivity to Simple Automated Trader-Agents (March 30, 2022). To appear in: A.-P. Rocha, L. Steels, & J. van den Herik (editors) Agents and Artificial Intelligence: Selected Papers from ICAART2021, Springer, 2022. , Available at SSRN: https://ssrn.com/abstract=4070885

  15. D. Cliff (2023) _Parameterised-Response Zero Intelligence (PRZI) Traders. Journal of Economic Interaction and Coordination, https://doi.org/10.1007/s11403-023-00388-7. https://link.springer.com/article/10.1007/s11403-023-00388-7

  16. D. Cliff (2023) Recurrence-Plot Visualization and Quantitative Analysis of Long-Term Co-Evolutionary Dynamics in a Simulated Financial Market with ZIP Traders. In: Proceedings of the 20th International Conference on Modeling, Simulation, and Visualization Methods (MSV'23) Las Vegas, July 2023. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4495631.

  17. D. Cliff (2023) Co-Evolution Causes Instability: Differential Evolution of ZIP Automated Traders in a Simulated Financial Market. In Proceedings of the 35th European Modeling and Simulation Symposium (EMSS23). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4480814

  18. A. Guarino, L. Grilli, D. Santoro, F. Messina, & R. Zaccagnino (2022), To learn or not to learn? Evaluating autonomous, adaptive, automated traders in cryptocurrencies financial bubbles. Neural Computing and Applications, 34:20715–20756 (2022)

  19. H. Hanifan, B. Watson, J. Cartlidge, & D. Cliff, (2021). Time Matters: Exploring the Effects of Urgency and Reaction Speed in Automated Traders. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence: selected revised papers from ICAART 2020. Lecture Notes in Computer Science, vol 12613. Springer. https://doi.org/10.1007/978-3-030-71158-0. https://link.springer.com/chapter/10.1007/978-3-030-71158-0_7

  20. G. Herbert (2023) Differential Weight and Population Size of PRDE Traders: An Analysis of Their Impact on Market Dynamics. In: Proceedings of the 15th International Conference on Agents and Artificial Intelligence (ICAART 2023) - Volume 1, pages 135-144. Available from ScitePress: https://www.scitepress.org/Papers/2023/118855/118855.pdf

  21. C. Johnson (2023) Building Technological Improvisation Skills through Student-devised Coursework Topics. Proceedings of the ACM Conference on Global Computing Education (CompEd2023) Vol 1, pages 91–97. ACM Press. https://dl.acm.org/doi/10.1145/3576882.3617917

  22. A. le Calvez & D. Cliff (2018) Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market. In: Proceedings of the IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr), Bengaluru. https://arxiv.org/pdf/1811.02880.pdf.

  23. B. Liu & J. Cartlidge (2022) Nonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial Market. In: Proceedings of the 35th European Modeling & Simulation Symposium (EMSS2023). Available from Arxiv: https://arxiv.org/pdf/2208.02901.pdf

  24. K. Lomas & D. Cliff (2021) Exploring Narrative Economics: An Agent-Based-Modeling Platform that Integrates Automated Traders with Opinion Dynamics. In: Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART2021). Available at Arxiv: https://arxiv.org/abs/2012.08840

  25. B. Miles & D. Cliff (2019) A Cloud-Native Globally Distributed Financial Exchange Simulator for Studying Real-World Trading-Latency Issues at Planetary Scale. In: Proceedings of the 31st European Modelling and Simulation Symposium (EMSS2019). Availabvle on Arxiv: https://arxiv.org/pdf/1909.12926.pdf

  26. M. Rollins & D. Cliff (2020) Which Trading Agent is Best? Using a Threaded Parallel Simulation of a Financial Market Changes the Pecking-Order. In: Proceedings of the 32nd European Modelling and Simulation Symposium (EMSS2020). Available at Arxiv: https://arxiv.org/pdf/2009.06905.pdf

  27. D. Snashall & D. Cliff (2019) Adaptive-Aggressive Traders Don't Dominate. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence: selected revised papers from ICAART 2019. Lecture Notes in Computer Science, Springer. https://doi.org/10.1007/978-3-030-71158-0. https://arxiv.org/pdf/1910.09947.pdf

  28. D. Savidge & D. Cliff, (2023) Simulation Studies of Automated Trading Algorithms for Financial Exchanges Operating Frequent Batch Auctions. In: Proceedings of the 35th European Modeling & Simulation Symposium (EMSS2023). Available at SSRN: https://ssrn.com/abstract=4502819

  29. D. Sawarnkatat & S. Smanchat (2022) NAGA: multi-blockchain based decentralized platform architecture for cryptocurrency payment. International Journal of Electrical and Computer Engineering (IJECE), 12(4):4067-4078. https://ijece.iaescore.com/index.php/IJECE/article/view/24782

  30. P. Shinde, I. Boukas, D. Radu, M. de Villena, & M. Amelin (2021), Analyzing Trade in Continuous Intra-Day Electricity Market: An Agent-Based Modeling Approach. Energies 14, 3860. https://www.mdpi.com/1996-1073/14/13/3860

  31. C. Van Oort, E. Ratliff-Crain, B. Tivnan, & S. Wshah (2023), Adaptive Agents and Data Quality in Agent-Based Financial Markets. Available on Arxiv: https://arxiv.org/pdf/2311.15974.pdf.

  32. A. Wray, M. Meades, & D. Cliff (2020) Automated Creation of a High-Performing Algorithmic Trader via Deep Learning on Level-2 Limit Order Book Data. In: Proceedings of the IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr2020). Available at Arxiv: https://arxiv.org/pdf/2012.00821.pdf

Add a custom footer
Add a custom sidebar
Clone this wiki locally