# New approaches in agent-based modeling of complex financial systems

## TL;DR

This paper reviews recent advances in agent-based modeling of financial systems, emphasizing data-driven parameter estimation and a novel big-data integrated modeling paradigm for simulating market dynamics.

## Contribution

It introduces a new approach combining big-data analysis with agent-based models to better simulate complex financial market behaviors.

## Key findings

- Models explain microscopic origins of market correlations
- Data-driven parameter estimation improves model realism
- Big-data integration enhances simulation of market dynamics

## 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 the 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 heterogenous personal preferences and interactions, these models are successful to explain the microscopic origination of the temporal and spatial correlations of the financial markets. We then present a novel paradigm combining the big-data analysis with the 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 the complex financial systems.