Generative Model Architecture Effects on Synthetic Tabular Data Fidelity and Downstream Classification in Imbalanced OpenML-CC18

Abstract

Abstract: Synthetic financial data provides a practical solution to the privacy, accessibility, and reproducibility challenges that often constrain empirical research in quantitative finance. This paper investigates the use of deep generative models, specifically Time-series Generative Adversarial Networks (TimeGAN) and Variational Autoencoders (VAEs) to generate realistic synthetic financial return series for portfolio construction and risk modeling applications. Using historical daily returns from the S and P 500 as a benchmark, we generate synthetic datasets under comparable market conditions and eva

Research Question

How do different generative model architectures (e.g., diffusion models vs. VAEs vs. GANs) affect the fidelity of synthetic tabular data and the resulting downstream classifier performance on imbalanced OpenML-CC18 tasks when controlling for training sample size?

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