Towards Financial World Modeling

Key points

• Building a world model requires a state representation useful for planning and decision-making---potentially over tasks unknown at training time.1
• In the context of financial markets, planning and decision-making may require a model to reason about market-wide conditions, asset-specific expected returns, liquidity, volatility, and cross-asset relationships.1
• We evaluate learned representations both by their predictive utility on common finance tasks and through probes of latent structure.1
• We find that encoders with similar predictive performance can organize market state very differently.1

Sources (1)

• [1]Towards Financial World Modeling
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 07:54 PM

  First, we introduce Market-1T, a dataset containing nearly one trillion observations across U.S. equities from 2008 to 2025 at 1 Hz resolution.

  Building a world model requires a state representation useful for planning and decision-making---potentially over tasks unknown at training time.

Extractive summary: sentences quoted from the sources.