Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Listening to Chaotic Whispers: A Deep Learning Framework for
  News-oriented Stock Trend Prediction   Stock trend prediction plays a critical role in seeking maximized profit from
stock investment. However, precise trend prediction is very difficult since the
highly volatile and non-stationary nature of stock market. Exploding
information on Internet together with advancing development of natural language
processing and text mining techniques have enable investors to unveil market
trends and volatility from online content. Unfortunately, the quality,
trustworthiness and comprehensiveness of online content related to stock market
varies drastically, and a large portion consists of the low-quality news,
comments, or even rumors. To address this challenge, we imitate the learning
process of human beings facing such chaotic online news, driven by three
principles: sequential content dependency, diverse influence, and effective and
efficient learning. In this paper, to capture the first two principles, we
designed a Hybrid Attention Networks to predict the stock trend based on the
sequence of recent related news. Moreover, we apply the self-paced learning
mechanism to imitate the third principle. Extensive experiments on real-world
stock market data demonstrate the effectiveness of our approach.

12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph