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Predicting the Stock movement attracts much attention from both industry andacademia. Despite such significant efforts, the results remain unsatisfactorydue to the inherently complicated nature of the stock market driven by factorsincluding supply and demand, the state of the economy, the political climate,and even irrational human behavior. Recently, Generative Adversarial Networks(GAN) have been extended for time series data; however, robust methods areprimarily for synthetic series generation, which fall short for appropriatestock prediction. This is because existing GANs for stock applications sufferfrom mode collapse and only consider one-step prediction, thus underutilizingthe potential of GAN. Furthermore, merging news and market volatility areneglected in current GANs. To address these issues, we exploit expert domainknowledge in finance and, for the first time, attempt to formulate stockmovement prediction into a Wasserstein GAN framework for multi-step prediction.We propose IndexGAN, which includes deliberate designs for the inherentcharacteristics of the stock market, leverages news context learning tothoroughly investigate textual information and develop an attentive seq2seqlearning network that captures the temporal dependency among stock prices,news, and market sentiment. We also utilize the critic to approximate theWasserstein distance between actual and predicted sequences and develop arolling strategy for deployment that mitigates noise from the financial market.Extensive experiments are conducted on real-world broad-based indices,demonstrating the superior performance of our architecture over otherstate-of-the-art baselines, also validating all its contributing components.

Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network