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New approaches in agent-based modeling of complex financial systems

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Abstract

Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents’ behaviors with heterogeneous personal preferences and interactions, these models are successful in explaining the microscopic origination of the temporal and spatial correlations of financial markets. We then present a novel paradigm combining big-data analysis with agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces and develop an agent-based model to simulate the dynamic behaviors of complex financial systems.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 11375149 and 11505099.

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Authors and Affiliations

  1. Department of Physics, Zhejiang University, Hangzhou, 310027, China

    Ting-Ting Chen, Bo Zheng, Yan Li & Xiong-Fei Jiang

  2. Collaborative Innovation Center of Advanced Microstructures, Nanjing, 210093, China

    Ting-Ting Chen, Bo Zheng, Yan Li & Xiong-Fei Jiang

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  1. Ting-Ting Chen
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  2. Bo Zheng
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Correspondence to Bo Zheng.

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arXiv: 1703.06840.

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Chen, TT., Zheng, B., Li, Y. et al. New approaches in agent-based modeling of complex financial systems. Front. Phys. 12, 128905 (2017). https://doi.org/10.1007/s11467-017-0661-2

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