Paper: arXiv 2610.01897

Authors: Victoria Ruojie Li, Arka Prava Bandyopadhyay

Abstract

We study whether model diversity survives selection into trading. In synthetic markets with a fixed mixture of three language-model families, news presentation changes their representation among submitted orders. At the announcement round, Qwen’s share of submitted orders shifts by 48 percentage points in the financing event, with little change in net order counts. In the workforce-reduction event, Mistral’s share shifts by 40 percentage points while net counts reverse sign. Homogeneous populations remove opposing flow when their active decisions share a direction. An analytical decomposition shows why selection can improve or worsen price accuracy even at unchanged aggregate demand sensitivity. The evidence concerns presentation bundles and submitted flow; cleaner replication and a known-value validation are specified prospectively.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a novel approach to studying model diversity in trading using synthetic markets and LLM agents. It demonstrates strong empirical rigor through controlled experiments and statistical analysis of order flow, while also providing an analytical decomposition for price accuracy. The methodology is clearly outlined, though full reproducibility would benefit from direct code/data links.

Research Flowchart

  flowchart TD
    A[Research Goal: Model Diversity & Selection into Trading?] --> B{Methodology: Synthetic Markets & News Presentation};
    B --> C{Data/Inputs: 3 LLM Families, News Events (Financing, Workforce Reduction)};
    C --> D[Computational Processes: Analyze Order Flow Shifts, Net Order Counts, Analytical Decomposition];
    D --> E[Key Findings: News Presentation Alters Model Representation in Order Flow; Selection Can Impact Price Accuracy (Positively/Negatively) Independently of Aggregate Demand Sensitivity];

Cited References (17)

  1. When Transparency Improves, Must Prices Reflect Fundamentals Better? (2018) — link
  2. Generative AI enhances individual creativity but reduces the collective diversity of novel content (2024) — link
  3. The Causal Impact of Media in Financial Markets (2011) — link
  4. Information Diversity and Complementarities in Trading and Information Acquisition (2015) — link
  5. Empirical Asset Pricing via Machine Learning (2020) — link
  6. Does Algorithmic Trading Improve Liquidity? (2011) — link
  7. Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models (2026) — link
  8. All the News That’s Fit to Reprint: Do Investors React to Stale Information? (2011) — link
and 9 more
  1. Assessing Look-Ahead Bias in Stock Return Predictions Generated by GPT Sentiment Analysis — → 2309.17322
  2. Can Large Language Models Trade? Testing Financial Theories with LLM Agents in Market Simulations (2025) — link
  3. Machine Forecast Disagreement (2026) — link
  4. Does Generative AI Facilitate Investor Trading? Early Evidence from ChatGPT Outages (2025) — link
  5. AI-Powered Trading, Algorithmic Collusion, and Price Efficiency (2025) — link
  6. LLM Agents Do Not Replicate Human Market Traders: Evidence From Experimental Finance (2025) — link
  7. Market Signal Injection: Adversarial Context Manipulation of LLM Pricing Agents (2026) — link
  8. Provider-Dependent Behavioral Asymmetries in LLM Trading Agents: Evidence from the Brazilian Equity Market (2026) — link
  9. Estimating means of bounded random variables by betting (2020) — link

1 of 17 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.