arXiv:2606.05138v1 Announce Type: new Abstract: Generating realistic financial time series is challenging as training data is often limited to a single historical path. With such scarce data, overfitting is hard to avoid, especially under adversarial training where a trained discriminator can memorize the training samples. To mitigate this, recent approaches train generators to minimize the discrepancy between untrained feature representations of real and generated time series. In these works, the feature maps are based on path signatures, which can fail to capture relevant time series propert
The paper addresses a core challenge in generative AI development for finance by proposing a method to create realistic financial time series despite limited historical data, directly tackling overfitting issues prevalent in adversarial training.
This breakthrough allows for more accurate and diverse synthetic financial data generation, which is crucial for training effective AI models in trading, risk management, and quantitative finance without relying on scarce real-world historical paths.
The ability to generate high-fidelity synthetic financial data reduces reliance on single historical paths and mitigates overfitting, enabling more robust AI model development and potentially new financial product design.
- · Quantitative Finance Departments
- · AI/ML Research Firms
- · Fintech Startups
- · Trading Algorithm Developers
- · Traditional Data Vendors
- · Models reliant on vast historical datasets
Improved synthetic data leads to more sophisticated and resilient AI models for financial forecasting and strategy.
The proliferation of more diverse and realistic synthetic data could democratize access to advanced financial AI, reducing the barrier to entry for smaller firms.
Enhanced AI capabilities in finance may lead to increased market efficiency or new forms of systemic risk if models become too interconnected.
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Read at arXiv cs.LG