Paper: arXiv 2410.14587

Abstract

Deep generative models are becoming increasingly used as tools for financial analysis. However, it is unclear how these models will influence financial markets, especially when they infer financial value in a semi-autonomous way. In this work, we explore the interplay between deep generative models and market dynamics. We develop a form of virtual traders that use deep generative models to make buy/sell decisions, which we term neuro-symbolic traders, and expose them to a virtual market. Under our framework, neuro-symbolic traders are agents that use vision-language models to discover a model of the fundamental value of an asset. Agents develop this model as a stochastic differential equation, calibrated to market data using gradient descent. We test our neuro-symbolic traders on both synthetic data and real financial time series, including an equity stock, commodity, and a foreign exchange pair. We then expose several groups of neuro-symbolic traders to a virtual market environment. This market environment allows for feedback between the traders belief of the underlying value to the observed price dynamics. We find that this leads to price suppression compared to the historical data, highlighting a future risk to market stability. Our work is a first step towards quantifying the effect of deep generative agents on markets dynamics and sets out some of the potential risks and benefits of this approach in the future.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematical modeling including stochastic differential equations (SDEs) and gradient-based calibration, placing it in the high math complexity range, while also conducting experiments on real financial time series with a virtual market environment, indicating substantial empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Quantify Impact of Deep Generative<br>Agents on Market Dynamics"] --> B{"Methodology"}
  B --> C["Data Inputs<br>(Synthetic & Real: Equity, Commodity, Forex)"]
  B --> D["Create Neuro-Symbolic Traders:<br>Vision-Language Models calibrating<br>Stochastic Differential Equations via Gradient Descent"]
  C --> E["Virtual Market Environment<br>with Price Feedback Loop"]
  D --> E
  E --> F["Computational Simulation:<br>Agents make buy/sell decisions<br>based on inferred fundamental value"]
  F --> G["Key Findings/Outcomes:<br>1. Price suppression observed<br>2. Potential risk to market stability<br>3. Framework for future analysis"]

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1 of 50 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.