MarketGPT: Developing a Pre-trained transformer (GPT) for Modeling Financial Time Series

Aaron Wheeler and Jeffrey D. Varner

Papers from arXiv.org

Abstract: This work presents a generative pre-trained transformer (GPT) designed for modeling financial time series. The GPT functions as an order generation engine within a discrete event simulator, enabling realistic replication of limit order book dynamics. Our model leverages recent advancements in large language models to produce long sequences of order messages in a steaming manner. Our results demonstrate that the model successfully reproduces key features of order flow data, even when the initial order flow prompt is no longer present within the model's context window. Moreover, evaluations reveal that the model captures several statistical properties, or 'stylized facts', characteristic of real financial markets and broader macro-scale data distributions. Collectively, this work marks a significant step toward creating high-fidelity, interactive market simulations.

Date: 2024-11
New Economics Papers: this item is included in nep-ain and nep-cmp
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