Journal of Advanced Robotics, Autonomous Systems and Human-Machine Interaction
Hedge Fund Replication with Deep Neural Networks and Generative Adversarial Networks
Abstract
Devin M. Chatterji
This study investigates whether modern deep learning techniques can improve hedge fund replication by modeling the nonlinear, dynamic, and regime-dependent relationships that characterize hedge fund returns more effectively than traditional linear factor models. Monthly returns from the Hennessee Hedge Fund Index (HHFI) and twenty-one hedge fund strategies were analyzed using fixed-weight and rolling-window regression models, deep neural networks (DNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), and generative adversarial networks (GAN). An expanded universe of sixty liquid financial factors was incorporated into the generative adversarial network framework to enhance factor selection and portfolio construction. Model performance was evaluated using out-of- sample tracking error together with paired statistical tests to determine whether observed differences in performance were statistically significant. The deep learning models consistently outperformed traditional regression approaches across the majority of hedge fund strategies. Among the conventional neural network architectures, recurrent neural networks generally achieved the lowest tracking error. The generative adversarial network models produced the strongest overall performance, with the six-factor and fifteen-factor configurations delivering the most accurate replication results. In selected hedge fund strategies, tracking error was reduced by as much as ninety-six percent relative to traditional regression models, and the improvements over regression, recurrent neural networks, and long short-term memory networks were statistically significant. These findings demonstrate that adversarial learning provides a more effective framework for estimating the nonlinear and time-varying factor exposures that drive hedge fund returns than conventional linear methods. The proposed approach offers a scalable, transparent, and cost-effective methodology for constructing hedge fund replication portfolios while demonstrating the broader value of deep learning techniques for financial modeling, portfolio management, and asset allocation.