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PAMS: Platform for Artificial Market Simulations

Published 19 Sep 2023 in q-fin.CP, cs.AI, cs.LG, and cs.MA | (2309.10729v1)

Abstract: This paper presents a new artificial market simulation platform, PAMS: Platform for Artificial Market Simulations. PAMS is developed as a Python-based simulator that is easily integrated with deep learning and enabling various simulation that requires easy users' modification. In this paper, we demonstrate PAMS effectiveness through a study using agents predicting future prices by deep learning.

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References (39)
  1. T. Lux and M. Marchesi, “Scaling and Criticality in a Stochastic Multi-agent Model of a Financial Market,” Nature, vol. 397, no. 6719, pp. 498–500, 1999.
  2. K. Kim, “Financial Time Series Forecasting using Support Vector Machines,” Neurocomputing, vol. 55, pp. 307–319, 2003.
  3. A. N. Kercheval and Y. Zhang, “Modelling High-frequency Limit Order Book Dynamics with Support Vector Machines,” Quantitative Finance, vol. 15, no. 8, pp. 1315–1329, 2015.
  4. J. A. Sirignano, “Deep Learning for Limit Order Books,” Quantitative Finance, vol. 19, no. 4, pp. 549–570, 2019.
  5. A. Tsantekidis, N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Using Deep Learning to Detect Price Change Indications in Financial Markets,” in Proceedings of the 25th European Signal Processing Conference, 2017, pp. 2580–2584.
  6. M. F. Dixon, N. G. Polson, and V. O. Sokolov, “Deep Learning for Spatio‐temporal Modeling: Dynamic Traffic Flows and High Frequency Trading,” Quantitative Finance, vol. 19, no. 4, pp. 549–570, 2019.
  7. L. Zhang, C. Aggarwal, and G.-J. Qi, “Stock Price Prediction via Discovering Multi-Frequency Trading Patterns,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, pp. 2141–2149.
  8. I. Maeda, D. deGraw, M. Kitano, H. Matsushima, H. Sakaji, K. Izumi, and A. Kato, “Deep Reinforcement Learning in Agent Based Financial Market Simulation,” Journal of Risk and Financial Management, vol. 13, no. 4, p. 71, 2020. [Online]. Available: https://www.mdpi.com/1911-8074/13/4/71
  9. M. Hirano, K. Izumi, and H. Sakaji, “Implementation of Actual Data for Artificial Market Simulation,” in Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022, pp. 1624–1626. [Online]. Available: https://doi.org/10.1007/s40844-015-0024-z
  10. M. Hirano, K. Izumi, and H. Sakaji, “Data-driven Agent Design for Artificial Market Simulation,” in Proceedings of the 36th Annual Conference of the Japanese Society for Artificial Intelligence, 2022, pp. 2S4IS2b01–2S4IS2b01.
  11. M. Hirano and K. Izumi, “Quantitative Tuning of Artificial Market Simulation using Generative Adversarial Network,” in The 6th IEEE International Conference on Agents, 2022, pp. 12–17.
  12. T. Torii, T. Kamada, K. Izumi, and K. Yamada, “Platform Design for Large-scale Artificial Market Simulation and Preliminary Evaluation on the K Computer,” Artificial Life and Robotics, vol. 22, no. 3, pp. 301–307, 2017.
  13. T. Torii, K. Izumi, T. Kamada, H. Yonenoh, D. Fujishima, I. Matsuura, M. Hirano, and T. Takahashi, “Plham: Platform for Large-scale and High-frequency Artificial Market,” 2016, https://github.com/plham/plham.
  14. T. Torii, K. Izumi, T. Kamada, H. Yonenoh, D. Fujishima, I. Matsuura, M. Hirano, T. Takahashi, and P. Finnerty, “PlhamJ,” 2019, https://github.com/plham/plhamJ.
  15. H. Sato, Y. Koyama, K. Kurumatani, Y. Shiozawa, and H. Deguchi, “U-mart: A test bed for interdisciplinary research into agent-based artificial markets,” in Evolutionary Controversies in Economics.   Springer, 2001, pp. 179–190.
  16. W. B. Arthur, J. H. Holland, B. LeBaron, R. Palmer, and P. Tayler, “Asset Pricing under Endogenous Expectations in an Artificial Stock Market,” The Economy as an Evolving Complex System II, pp. 15–44, 1997.
  17. D. Byrd, M. Hybinette, T. Hybinette Balch, and J. Morgan, “ABIDES: Towards High-Fidelity Multi-Agent Market Simulation,” in Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, vol. 12, 2020, pp. 11–22.
  18. T. C. Schelling, “Models of segregation,” The American economic review, vol. 59, no. 2, pp. 488–493, 1969.
  19. R. Axelrod, “Effective choice in the prisoner’s dilemma,” Journal of conflict resolution, vol. 24, no. 1, pp. 3–25, 1980.
  20. R. Axelrod, “More effective choice in the prisoner’s dilemma,” Journal of conflict resolution, vol. 24, no. 3, pp. 379–403, 1980.
  21. M. Sajjad, K. Singh, E. Paik, and C. W. Ahn, “A data-driven approach for agent-based modeling: Simulating the dynamics of family formation,” Journal of Artificial Societies and Social Simulation, vol. 19, no. 1, p. 9, 2016.
  22. Y. Nonaka, M. Onishi, T. Yamashita, T. Okada, A. Shimada, and R. I. Taniguchi, “Walking velocity model for accurate and massive pedestrian simulator,” IEEJ Transactions on Electronics, Information and Systems, vol. 133, no. 9, pp. 1779–1786, 2013.
  23. K. Braun-Munzinger, Z. Liu, and A. E. Turrell, “An agent-based model of corporate bond trading,” Quantitative Finance, vol. 18, no. 4, pp. 591–608, 2018.
  24. S. Kurahashi, “Estimating Effectiveness of Preventing Measures for 2019 Novel Coronavirus Diseases (COVID-19),” Proceeding of 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020, pp. 487–492, 2020.
  25. S. M. Edmonds and Bruce, “Towards Good Social Science,” Journal of Artificial Societies and Social Simulation, vol. 8, no. 4, p. 13, 2005, https://www.jasss.org/8/4/13.html.
  26. J. D. Farmer and D. Foley, “The economy needs agent-based modelling,” Nature, vol. 460, no. 7256, pp. 685–686, 2009.
  27. S. Battiston, J. D. Farmer, A. Flache, D. Garlaschelli, A. G. Haldane, H. Heesterbeek, C. Hommes, C. Jaeger, R. May, and M. Scheffer, “Complexity theory and financial regulation: Economic policy needs interdisciplinary network analysis and behavioral modeling,” Science, vol. 351, no. 6275, pp. 818–819, 2016.
  28. R. Axelrod, “The complexity of cooperation,” in The Complexity of Cooperation.   Princeton university press, 1997, pp. 1–248.
  29. B. Edmonds and S. Moss, “From kiss to kids–an ‘anti-simplistic’modelling approach,” in International workshop on multi-agent systems and agent-based simulation.   Springer, 2004, pp. 130–144.
  30. T. Terano, “Agent-based modeling: Beyond the kiss principle (¡special issue¿ complex systems and collective intelligence),” Artificial Intelligence, vol. 18, no. 6, pp. 710–715, 2003.
  31. T. Mizuta, “An Agent-based Model for Designing a Financial Market that Works Well,” in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2019, pp. 400–406.
  32. J.-C. Trichet, “Reflections on the nature of monetary policy non-standard measures and financial study,” in Approaches to monetary policy revisited - lessons from the crisis, J. Marek, F. Smets, and C. Thimann, Eds.   European Central Bank, 2011, pp. 12–22.
  33. W. Cui and A. Brabazon, “An agent-based modeling approach to study price impact,” in Proceedings of 2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2012, 2012, pp. 241–248.
  34. T. Torii, K. Izumi, and K. Yamada, “Shock transfer by arbitrage trading: analysis using multi-asset artificial market,” Evolutionary and Institutional Economics Review, vol. 12, no. 2, pp. 395–412, 2015.
  35. T. Mizuta, S. Kosugi, T. Kusumoto, W. Matsumoto, K. Izumi, I. Yagi, and S. Yoshimura, “Effects of Price Regulations and Dark Pools on Financial Market Stability: An Investigation by Multiagent Simulations,” Intelligent Systems in Accounting, Finance and Management, vol. 23, no. 1-2, pp. 97–120, 2016.
  36. M. Hirano, K. Izumi, T. Shimada, H. Matsushima, and H. Sakaji, “Impact Analysis of Financial Regulation on Multi-Asset Markets Using Artificial Market Simulations,” Journal of Risk and Financial Management, vol. 13, no. 4, p. 75, 2020.
  37. S. J. Leal and M. Napoletano, “Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading,” Journal of Economic Behavior and Organization, vol. 157, pp. 15–41, 2019.
  38. M. Paddrik, R. Hayes, A. Todd, S. Yang, P. Beling, and W. Scherer, “An agent based model of the E-Mini S&P 500 applied to flash crash analysis,” in Proceedings of 2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2012, 2012, pp. 257–264.
  39. J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer Normalization,” arXiv, 2016.
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