A Hybrid Approach on Conditional GAN for Portfolio Analysis
This work addresses portfolio analysis for financial markets, presenting an incremental improvement by combining deep generative models with conditional GANs.
The paper tackles the problem of portfolio analysis by introducing a hybrid conditional GAN approach that learns internal trends and models market uncertainty and future trends, showing that HybridCGAN and HybridACGAN lead to better portfolio allocation compared to existing methods like Markowitz, CGAN, and ACGAN on real-world datasets from US and Europe markets.
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.