Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating Realistic Stock Market Order Streams   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.
The generator design includes specially crafted aspects including components
that approximate the market's auction mechanism, augmenting the order history
with order-book constructions to improve the generation task. We perform an
ablation study to verify the usefulness of aspects of our network structure. We
provide a mathematical characterization of distribution learned by the
generator. We also propose statistics to measure the quality of generated
orders. We test our approach with synthetic and actual market data, compare to
many baseline generative models, and find the generated data to be close to
real data.

15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th