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# Multi-Agent Market Simulation with Spoofing Detection
- URL: https://fsc.stevens.edu/multi-2/
- Published: 2025-11-12T16:46:27.000Z
- Updated: 2026-05-16T22:35:27.000Z
- Author: Salvatore Matteis
- Tags: 2025 Spring, FE 800 Projects

## **Researcher**

Sidharth Koduru

## **Faculty Advisor**

Dr. Steve Yang

## **Overview**

This study develops a multi-agent financial-market simulator to examine how spoofing—the rapid placement and cancellation of deceptive orders—affects market dynamics and how AI systems might detect it.  
The simulator models fundamentalist, chartist, zero-intelligence, and spoofing agents trading in a continuous double-auction limit-order-book.  
Two spoofing-detection frameworks are compared: a Q-learning reinforcement-learning (RL) detector and a GPT-4-based detector that uses large-language-model reasoning on simulated market data.

## **Key Findings**

### **1\. Market Validation**.

- The simulation successfully reproduces core stylized facts of financial returns:
  - Heavy-tailed short-horizon returns and aggregational Gaussianity at longer horizons.
  - Near-zero autocorrelation of returns.
  - Volatility clustering and a mild leverage effect.
- These results confirm that heterogeneous interacting agents can replicate realistic market statistics.

### **2\. Impact of Spoofing**

- Introducing spoofing agents increases price volatility and amplifies heavy-tailed behavior.
- Spoofers profit by momentarily distorting price levels, while fundamentalists and chartists experience losses during manipulation phases.
- Overall, spoofing disrupts equilibrium and market efficiency, validating its destabilizing role.

### **3\. Detector Methodology**

- **RL Detector:**Learns to classify states (“spoofing” vs “normal”) via Q-learning on four engineered features—price change, volume imbalance, volatility, and order-cancellation rate.Achieved ≈ 99 % accuracy but F1 = 0, meaning it rarely identified true spoofing due to extreme class imbalance.
- **GPT-4 Detector:**Receives text-based prompts describing market metrics and spoofing cues, then outputs a yes/no judgment.Produced no positive detections (F1 = 0), demonstrating that single-snapshot reasoning is insufficient without sequential context or fine-tuning.

### **4\. Comparative Insights**

- Both detectors showed high apparent accuracy but failed on recall—typical of imbalanced-data problems.
- RL learned conservative “no-spoof” behavior; GPT-4 defaulted to cautious negatives.
- Spoofing detection likely requires temporal context, reward re-weighting, or ensemble methods to balance false positives and negatives.

### **Conclusion**

The integrated ABM framework realistically simulates market behavior and manipulation, but AI-based spoofing detection remains unreliable under sparse, noisy conditions.  
Future research should:

- Introduce sequence-based or deep-RL models capturing multi-tick patterns.
- Re-weight rewards or oversample spoofing to improve recall.
- Explore fine-tuned language models and sentiment/news integration for richer state representation.

### **Significance**

This project bridges computational-finance simulation and AI-driven market-surveillance, showing how combining ABM and modern AI can advance understanding of market manipulation mechanisms and inform the design of robust spoofing-detection systems for regulators and exchanges.