Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A parallel-network continuous quantitative trading model with GARCH and
  PPO   It is a difficult task for both professional investors and individual traders
continuously making profit in stock market. With the development of computer
science and deep reinforcement learning, Buy\&Hold (B\&H) has been oversteped
by many artificial intelligence trading algorithms. However, the information
and process are not enough, which limit the performance of reinforcement
learning algorithms. Thus, we propose a parallel-network continuous
quantitative trading model with GARCH and PPO to enrich the basical deep
reinforcement learning model, where the deep learning parallel network layers
deal with 3 different frequencies data (including GARCH information) and
proximal policy optimization (PPO) algorithm interacts actions and rewards with
stock trading environment. Experiments in 5 stocks from Chinese stock market
show our method achieves more extra profit comparing with basical reinforcement
learning methods and bench models.

34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th