Generating Realistic Stock Market Order Streams

ICLR 2019 Junyi LiXitong WangYaoyang LinArunesh SinhaMicheal P. Wellman

We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders... (read more)

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