Open AccessAn Attention-Based LSTM Model for Stock Price Trend Prediction Using Limit Order Books
Author(s) -
Yunhao Li,
Liuliu Li,
Xudong Zhao,
Tianyi Ma,
Ying Zou,
Ming Chen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1575/1/012124
Subject(s) - stock (firearms) , stock price , computer science , econometrics , volatility (finance) , cost price , stock market , financial economics , economics , mechanical engineering , paleontology , horse , series (stratigraphy) , engineering , biology
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.