Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks.
We model the order stream as a stochastic process with finite history dependence, and employ a conditional Wasserstein GAN to capture history dependence of orders in a stock market.
We test our approach with actual market and synthetic data on a number of different statistics, and find the generated data to be close to real data.
A:
We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks.