A Hybrid Approach on Conditional GAN for Portfolio Analysis

TL;DR

This paper introduces a hybrid conditional GAN model that simultaneously learns historical trends and market uncertainty, improving portfolio allocation over traditional methods and existing generative models.

Contribution

A novel hybrid conditional GAN framework that effectively captures both internal trends and market uncertainty for enhanced portfolio analysis.

Findings

Hybrid models outperform Markowitz, CGAN, and ACGAN in portfolio allocation.

Models demonstrate improved prediction of future market trends.

Evaluation on US and European datasets confirms effectiveness.

Abstract

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), conditional GAN (CGAN), and autoencoding CGAN (ACGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN or ACGAN framework stands in putting too much emphasis on generating series and finding the internal trends of the series rather than predicting the future trends. In this paper, we introduce a hybrid approach on conditional GAN 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 HybridCGAN and HybridACGAN models lead to better portfolio allocation compared to the existing Markowitz, CGAN, and ACGAN approaches.