MarketGPT: Developing a Pre-trained transformer (GPT) for Modeling Financial Time Series
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909403944321024 |
|---|---|
| author | Wheeler, Aaron Varner, Jeffrey D. |
| author_facet | Wheeler, Aaron Varner, Jeffrey D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16585 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MarketGPT: Developing a Pre-trained transformer (GPT) for Modeling Financial Time Series Wheeler, Aaron Varner, Jeffrey D. Trading and Market Microstructure 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. |
| title | MarketGPT: Developing a Pre-trained transformer (GPT) for Modeling Financial Time Series |
| topic | Trading and Market Microstructure |
| url | https://arxiv.org/abs/2411.16585 |