Group-wise herding behavior in financial markets: an agent-based modeling approach

Area of Science:

• Financial market dynamics
• Behavioral economics
• Computational finance

Background:

• Understanding group behavior in financial markets is crucial for economic stability.
• The interplay between monetary policy and economic unit behavior requires sophisticated modeling.
• Existing models often simplify the spectrum of irrationality and its market impact.

Purpose of the Study:

• To investigate the dynamic characteristics of rational group behaviors in financial markets.
• To analyze the relationship between monetary policy (macro vs. micro) and economic units.
• To quantify the impact of different levels of irrationality on market behavior.

Main Methods:

• Utilizing an agent-based model (ABM) to simulate group interactions.
• Applying the Hurst exponent to assess trend-following behavior and long-term memory.
• Employing Shannon entropy to measure randomness and unpredictability in market dynamics.

Main Results:

• Macro-monetary policy systems exhibit steep fluctuations, with medium irrationality showing highest Hurst exponent and Shannon entropy.
• Micro-monetary policy systems lead to stable trends across all irrationality levels.
• Combined micro- and macro-monetary policy systems also result in stable group behaviors.

Conclusions:

• Group behavior in financial markets is contingent upon the type of monetary policy implemented.
• Monetary policy's focus (macro, micro, or combined) critically influences market stability and predictability.
• Findings provide essential insights for optimizing monetary policy strategies in financial markets.