Abstract
Synthetic financial data offers a practical way to address the privacy and accessibility challenges that limit research in quantitative finance. This paper examines the use of generative models, in particular TimeGAN and Variational Autoencoders (VAEs), for creating synthetic return series that support portfolio construction, trading analysis, and risk modeling. Using historical daily returns from the S and P 500 as a benchmark, we generate synthetic datasets under comparable market conditions and evaluate them using statistical similarity metrics, temporal structure tests, and downstream financial tasks. The study shows that TimeGAN produces synthetic data with distributional shapes, volatility patterns, and autocorrelation behaviour that are close to those observed in real returns. When applied to mean-variance portfolio optimization, the resulting synthetic datasets lead to portfolio weights, Sharpe ratios, and risk levels that remain close to those obtained from real data. The VAE provides more stable training but tends to smooth extreme market movements, which affects risk estimation. Finally, the analysis supports the use of synthetic datasets as substitutes for real financial data in portfolio analysis and risk simulation, particularly when models are able to capture temporal dynamics. Synthetic data therefore provides a privacy-preserving, cost-effective, and reproducible tool for financial experimentation and model development.
Abstract (translated)
合成金融数据为解决量化金融研究中隐私和可访问性限制提供了实际途径。本文探讨了生成模型（尤其是TimeGAN和变分自编码器(VAEs)）在创建支持投资组合构建、交易分析和风险建模的合成回报序列方面的应用。以标普500的历史每日收益为基准，我们生成了符合相似市场条件的合成数据集，并通过统计相似度指标、时间结构测试以及下游金融任务对其进行了评估。 研究表明，TimeGAN能够产生与实际回报观察到的分布形状、波动模式和自相关行为非常接近的合成数据。在均值-方差投资组合优化中应用这些合成数据后，生成的投资组合权重、夏普比率及风险水平仍然与使用真实数据所得的结果相近。相比之下，VAE提供更稳定的训练过程，但倾向于平滑极端市场变动，这影响了风险估计。 最终分析表明，在能够捕捉时间动态特性的模型下，可以将合成数据集作为实际金融数据的替代品用于投资组合分析和风险模拟中。因此，合成数据为金融实验与模型开发提供了隐私保护、成本效益以及可重复使用的工具。
URL
https://arxiv.org/abs/2512.21798
