Exploring the potential of Generative Adversarial Networks in Finance
Overview
This article is a summary of "Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination", which proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategies calibration and aggregation. The authors provide a methodology for training and selecting a cGAN for time series generation, and explain how each sample can be used for strategies calibration and how all generated samples can be used for ensemble modeling. The article presents a case study encompassing 579 assets, comparing cGAN Sampling and Aggregation with Stationary Bootstrap for fine-tuning and ensemble modeling of trading strategies. The authors also discuss potential extensions and directions for further research.
Summary
The article proposes the use of cGANs for trading strategies calibration and aggregation. The authors outline the advantages of using cGANs, including generating more diverse training and testing sets compared to traditional resampling techniques, drawing samples specifically about stressful events ideal for model checking and stress testing, and providing a level of anonymization to the dataset. The authors provide a full methodology for training and selecting a cGAN for time series generation, including strategies calibration and ensemble modeling. The article presents a case study comparing cGAN Sampling and Aggregation with Stationary Bootstrap for fine-tuning and ensemble modeling of trading strategies, with results showing cGAN outcompeted Stationary Bootstrap for combining weak signals in alpha generating strategies. The authors also suggest potential avenues for future research, such as using cGANs for stress testing and exploring different loss functions and architectures. Overall, the article highlights the potential of cGANs in the finance industry for generating new trading strategies and improving existing ones.