# Autoencoding Conditional GAN for Portfolio Allocation Diversification

arXiv: https://arxiv.org/abs/2207.05701 · HTML: https://mlsanity.com/paper/2207.05701/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [Matrix Decomposition and Applications](https://mlsanity.com/paper/2201.00145/) (2022)
- [Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization](https://mlsanity.com/paper/2205.11025/) (2022)
- [Reducing overestimating and underestimating volatility via the augmented blending-ARCH model](https://mlsanity.com/paper/2203.12456/) (2022)

## Built on by (most cited first, 3 of 3)

- [Implementing Quantum Generative Adversarial Network (qGAN) and QCBM in Finance](https://mlsanity.com/paper/2308.08448/) (2023)
- [A Hybrid Approach on Conditional GAN for Portfolio Analysis](https://mlsanity.com/paper/2208.07159/) (2022)
- [Generative Models: Principles, Architectures, and Applications](https://mlsanity.com/paper/2608.08101/) (2026)

## Abstract (arXiv)

Over the decades, the Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on the analysis of the market uncertainty rather than on the trend prediction. While generative adversarial network (GAN) and conditional GAN (CGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN framework stands in putting too much emphasis on generating series rather than keeping features that can help this generator. In this paper, we introduce an autoencoding CGAN (ACGAN) based on deep generative models that learns the internal trend of historical data while modeling market uncertainty and future trends. We evaluate the model on several real-world datasets from both the US and Europe markets, and show that the proposed ACGAN model leads to better portfolio allocation and generates series that are closer to true data compared to the existing Markowitz and CGAN approaches.