Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mid-price Prediction Based on Machine Learning Methods with Technical
  and Quantitative Indicators   Stock price prediction is a challenging task, but machine learning methods
have recently been used successfully for this purpose. In this paper, we
extract over 270 hand-crafted features (factors) inspired by technical and
quantitative analysis and tested their validity on short-term mid-price
movement prediction. We focus on a wrapper feature selection method using
entropy, least-mean squares, and linear discriminant analysis. We also build a
new quantitative feature based on adaptive logistic regression for online
learning, which is constantly selected first among the majority of the proposed
feature selection methods. This study examines the best combination of features
using high frequency limit order book data from Nasdaq Nordic. Our results
suggest that sorting methods and classifiers can be used in such a way that one
can reach the best performance with a combination of only very few advanced
hand-crafted features.

20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org