MarketGANs: Multivariate financial time-series data augmentation using generative adversarial networks
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918303808618496 |
|---|---|
| author | Huh, Jeonggyu Jeong, Seungwon Kim, Hyun-Gyoon Koo, Hyeng Keun Lim, Byung Hwa |
| author_facet | Huh, Jeonggyu Jeong, Seungwon Kim, Hyun-Gyoon Koo, Hyeng Keun Lim, Byung Hwa |
| contents | This paper introduces MarketGAN, a factor-based generative framework for high-dimensional asset return generation under severe data scarcity. We embed an explicit asset-pricing factor structure as an economic inductive bias and generate returns as a single joint vector, thereby preserving cross-sectional dependence and tail co-movement alongside inter-temporal dynamics. MarketGAN employs generative adversarial learning with a temporal convolutional network (TCN) backbone, which models stochastic, time-varying factor loadings and volatilities and captures long-range temporal dependence. Using daily returns of large U.S. equities, we find that MarketGAN more closely matches empirical stylized facts of asset returns, including heavy-tailed marginal distributions, volatility clustering, leverage effects, and, most notably, high-dimensional cross-sectional correlation structures and tail co-movement across assets, than conventional factor-model-based bootstrap approaches. In portfolio applications, covariance estimates derived from MarketGAN-generated samples outperform those derived from other methods when factor information is at least weakly informative, demonstrating tangible economic value. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17773 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MarketGANs: Multivariate financial time-series data augmentation using generative adversarial networks Huh, Jeonggyu Jeong, Seungwon Kim, Hyun-Gyoon Koo, Hyeng Keun Lim, Byung Hwa Statistical Finance Machine Learning Econometrics This paper introduces MarketGAN, a factor-based generative framework for high-dimensional asset return generation under severe data scarcity. We embed an explicit asset-pricing factor structure as an economic inductive bias and generate returns as a single joint vector, thereby preserving cross-sectional dependence and tail co-movement alongside inter-temporal dynamics. MarketGAN employs generative adversarial learning with a temporal convolutional network (TCN) backbone, which models stochastic, time-varying factor loadings and volatilities and captures long-range temporal dependence. Using daily returns of large U.S. equities, we find that MarketGAN more closely matches empirical stylized facts of asset returns, including heavy-tailed marginal distributions, volatility clustering, leverage effects, and, most notably, high-dimensional cross-sectional correlation structures and tail co-movement across assets, than conventional factor-model-based bootstrap approaches. In portfolio applications, covariance estimates derived from MarketGAN-generated samples outperform those derived from other methods when factor information is at least weakly informative, demonstrating tangible economic value. |
| title | MarketGANs: Multivariate financial time-series data augmentation using generative adversarial networks |
| topic | Statistical Finance Machine Learning Econometrics |
| url | https://arxiv.org/abs/2601.17773 |