Fin-GAN: forecasting and classifying financial time series via generative adversarial networks
研究用生成对抗网络（GAN）对金融时间序列进行概率预测，设计了一种经济学驱动的损失函数，使GAN更适合分类任务并实现监督学习，在股票数据上比LSTM和ARIMA获得更高夏普比率。
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.