Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CorrGAN: Sampling Realistic Financial Correlation Matrices Using
  Generative Adversarial Networks   We propose a novel approach for sampling realistic financial correlation
matrices. This approach is based on generative adversarial networks.
Experiments demonstrate that generative adversarial networks are able to
recover most of the known stylized facts about empirical correlation matrices
estimated on asset returns. This is the first time such results are documented
in the literature. Practical financial applications range from trading
strategies enhancement to risk and portfolio stress testing. Such generative
models can also help ground empirical finance deeper into science by allowing
for falsifiability of statements and more objective comparison of empirical
methods.

7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph