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Nonlinear scaling analysis approach of agent-based Potts financial dynamical model
1Institute of Financial Mathematics and Financial Engineering School of Science, Beijing Jiaotong University Beijing 100044, People's Republic of China.
Chaos (Woodbury, N.Y.)
|January 3, 2015
Summary
This study introduces a financial agent-based model using the Potts model from statistical physics to analyze market behavior. It reveals correlations in financial returns, comparing simulated data with actual stock market data.
Area of Science:
- Statistical physics
- Financial modeling
- Complex systems
Background:
- Agent-based models are increasingly used in finance.
- The Potts model offers a framework for agent interactions.
- Understanding return correlations is crucial for financial markets.
Purpose of the Study:
- To develop and investigate a financial agent-based price model using the Potts model.
- To analyze the correlation behavior of normalized returns.
- To compare the statistical behaviors of simulated returns with actual market data.
Main Methods:
- Utilized the Potts model, a statistical physics dynamic system.
- Applied power law classification scheme analysis.
- Employed empirical mode decomposition analysis.
- Analyzed daily returns of Shanghai Composite Index and Shenzhen Component Index.
Main Results:
- The Potts model successfully simulates agent interactions and market dynamics.
- Identified power-law correlations in normalized financial returns.
- Nonlinear analysis revealed similarities between simulated and actual market return behaviors.
Conclusions:
- The developed financial agent-based model provides insights into market dynamics.
- The Potts model is a viable tool for financial market simulation.
- Further research can explore more complex agent interactions and market conditions.
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