Paper: arXiv 2409.00742

Abstract

We propose that a tree-like hierarchical structure represents a simple and effective way to model the emergent behaviour of financial markets, especially markets where there exists a pronounced intersection between social media influences and investor behaviour. To explore this hypothesis, we introduce an agent-based model of financial markets, where trading agents are embedded in a hierarchical network of communities, and communities influence the strategies and opinions of traders. Empirical analysis of the model shows that its behaviour conforms to several stylized facts observed in real financial markets; and the model is able to realistically simulate the effects that social media-driven phenomena, such as echo chambers and pump-and-dump schemes, have on financial markets.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper primarily uses an agent-based model with a hierarchical network, which involves moderate algorithmic complexity but lacks heavy mathematical derivations or formal proofs. Empirical rigor is bolstered by explicit mention of open-source code availability on GitHub and analysis against stylized facts, but it’s based on simulated data rather than backtesting on real market data.

Research Flowchart

  flowchart TD
  A["Research Goal<br/>How does a hierarchical<br/>network structure model<br/>social media-driven bubbles?"] --> B["Methodology: Agent-Based Model<br/>- Agents in hierarchical community network<br/>- Social media influence dynamics"]
  B --> C["Inputs: Model Parameters<br/>- Network hierarchy<br/>- Influence weights<br/>- Trading rules"]
  C --> D["Computational Process<br/>Agent Interaction &<br/>Market Simulation"]
  D --> E["Key Findings<br/>- Conforms to stylized facts<br/>- Simulates echo chambers<br/>- Models pump-and-dump schemes<br/>- Validates hierarchical structure"]

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0 of 68 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.