Oxford-Man Institute: Fin-GAN - Forecasting via Generative Adversarial Networks

Can Generative Adversarial Networks (GANs) forecast financial time-series data? Milena Vuletić Postgraduate Student at the Oxford-Man Institute of Quantitative Finance shares her research findings.


Fin-GAN: Forecasting and Classifying Financial Time Series via Generative Adversarial Networks

Vuletić, Milena and Cucuringu, Mihai and Prenzel, Felix

We investigate the use of Generative Adversarial Networks (GANs) for probabilistic forecasting of financial time series. To this end, we introduce a novel economics-driven loss function for the generator. This newly designed loss function renders GANs more suitable for a classification task, and places them into a supervised learning setting, whilst producing full conditional probability distributions of price returns given previous historical values. Our approach moves beyond the point estimates traditionally employed in the forecasting literature, and allows for uncertainty estimates. Numerical experiments on equity data showcase the effectiveness of our proposed methodology, which achieves higher Sharpe Ratios compared to classical supervised learning models, such as LSTMs and ARIMA.


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