Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Applications of deep learning in stock market prediction: recent
  progress   Stock market prediction has been a classical yet challenging problem, with
the attention from both economists and computer scientists. With the purpose of
building an effective prediction model, both linear and machine learning tools
have been explored for the past couple of decades. Lately, deep learning models
have been introduced as new frontiers for this topic and the rapid development
is too fast to catch up. Hence, our motivation for this survey is to give a
latest review of recent works on deep learning models for stock market
prediction. We not only category the different data sources, various neural
network structures, and common used evaluation metrics, but also the
implementation and reproducibility. Our goal is to help the interested
researchers to synchronize with the latest progress and also help them to
easily reproduce the previous studies as baselines. Base on the summary, we
also highlight some future research directions in this topic.

32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th