arXiv:2605.27113v1 Announce Type: new Abstract: In recent years, financial institutions and firms have increasingly adopted synthetic data to address data scarcity and to generate counterfactual market scenarios. However, reproducing all the statistical properties of financial time series, commonly known as stylized facts, remains an open challenge for many existing general-purpose architectures. In this paper, we present a quality-aware generative framework that combines two classes of generative methods, demonstrating how their integration addresses existing limitations while enhancing the r
Advances in generative AI, particularly combining GANs and diffusion models, are reaching a maturity where they can effectively address complex data generation challenges like those in financial time series. The increasing demand from financial institutions for robust synthetic data further accelerates this development.
This development allows for more accurate and realistic synthetic financial data, mitigating data scarcity and enabling sophisticated counterfactual market scenario generation crucial for risk management, algorithmic trading, and financial product development. It could reduce reliance on sensitive real-world data.
Financial institutions gain a new, high-quality tool for synthetic data generation, potentially improving model training accuracy and reducing regulatory hurdles associated with real data use. This enhances analytical capabilities and accelerates innovation in finance.
- · Financial institutions
- · Quantitative analysts
- · AI/ML developers
- · Traditional data vendors (potentially)
- · Less sophisticated financial modeling techniques
Financial models, particularly those for risk and algorithmic trading, become more robust and adaptable due to enhanced synthetic data capabilities.
Increased use of synthetic data could lead to new regulatory frameworks for its validation and ethical application in financial markets.
The widespread adoption of high-quality synthetic data might inadvertently create new forms of market fragility if models trained exclusively on synthetic data fail to capture unforeseen real-world anomalies.
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