An Attention-Based LSTM Model for Stock Price Trend Prediction Using Limit Order Books

被引：2

作者：

Li, Yunhao ^{[1
,2
]}

Li, Liuliu ^{[3
]}

Zhao, Xudong ^{[3
]}

Ma, Tianyi ^{[2
,3
]}

Zou, Ying ^{[3
]}

Chen, Ming ^{[2
,3
]}

机构：

[1] Harbin Inst Technol, 92 West Dazhi St, Harbin, Peoples R China

[2] Zhejiang Univ, 38 Zheda Rd, Hangzhou, Peoples R China

[3] Hithink RoyalFlush Informat Network Co Ltd, Hangzhou, Peoples R China

来源：

5TH ANNUAL INTERNATIONAL CONFERENCE ON INFORMATION SYSTEM AND ARTIFICIAL INTELLIGENCE (ISAI2020) | 2020年 / 1575卷

关键词：

D O I：

10.1088/1742-6596/1575/1/012124

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Stock price trend prediction has been a hot issue in the financial field, which has been paid much attention by both academia and industry. It is a challenging task due to the non-stationary and high volatility of the stock prices. Traditional methods for predicting stock price trends are mostly based on the historical OHLC (i.e., open, high, low, and close prices) data. However, it eliminates most of the trading information. To address this problem, in this paper, another type of stock price data, i.e., limit order books (LOBs), is used. For better exploring the relationship of the LOBs and stock price trend, inspired by the successful application of deep learning-based methods, an attention-based LSTM model is applied. The trend of stock price can be predicted by using the LOBs data of the previous day. By using the real stock price data of the China stock market, the effectiveness of the proposed model is validated by experimental results.

引用

页数：7

共 50 条

[21] The Prediction Stock Market Price Using LSTM
Barik, Rhada
Baina, Amine
Bellafkih, Mostafa
EMERGING TRENDS IN INTELLIGENT SYSTEMS & NETWORK SECURITY, 2023, 147 : 444 - 453
[22] Early intention prediction of pedestrians using contextual attention-based LSTM
Jing Lian
Fengning Yu
Linhui Li
Yafu Zhou
Multimedia Tools and Applications, 2023, 82 : 14713 - 14729
[23] Early intention prediction of pedestrians using contextual attention-based LSTM
Lian, Jing
Yu, Fengning
Li, Linhui
Zhou, Yafu
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (10) : 14713 - 14729
[24] Stock Price Prediction using Combined LSTM-CNN Model
Zhou, Xinrong
2021 3RD INTERNATIONAL CONFERENCE ON MACHINE LEARNING, BIG DATA AND BUSINESS INTELLIGENCE (MLBDBI 2021), 2021, : 67 - 71
[25] Attention-based CNN-LSTM for high-frequency multiple cryptocurrency trend prediction
Peng, Peng
Chen, Yuehong
Lin, Weiwei
Wang, James Z.
EXPERT SYSTEMS WITH APPLICATIONS, 2024, 237
[26] Study on the prediction of stock price based on the associated network model of LSTM
Guangyu Ding
Liangxi Qin
International Journal of Machine Learning and Cybernetics, 2020, 11 : 1307 - 1317
[27] Parallel attention-based LSTM for building a prediction model of vehicle emissions using PEMS and OBD
Xie, Hao
Zhang, Yujun
He, Ying
You, Kun
Fan, Boqiang
Yu, Dongqi
Lei, Boen
Zhang, Wangchun
MEASUREMENT, 2021, 185
[28] Study on the prediction of stock price based on the associated network model of LSTM
Ding, Guangyu
Qin, Liangxi
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2020, 11 (06) : 1307 - 1317
[29] PREDICTION STOCK PRICE BASED ON DIFFERENT INDEX FACTORS USING LSTM
Lai, Chun Yuan
Chen, Rung-Ching
Caraka, Rezzy Eko
PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), 2019, : 416 - 421
[30] Two-channel Attention Mechanism Fusion Model of Stock Price Prediction Based on CNN-LSTM
Sun, Lin
Xu, Wenzheng
Liu, Jimin
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING, 2021, 20 (05)

← 1 2 3 4 5 →