Spatio-temporal Model for Limit Order Books in the Stock Exchange of Thailand

Conference proceedings article

Authors/Editors

Strategic Research Themes

No matching items found.

Publication Details

Author list: Thipprachak K., Tangamchit P.

Publisher: Hindawi

Publication year: 2019

Start page: 119

End page: 122

Number of pages: 4

ISBN: 9781538677742

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063296627&doi=10.1109%2fICA-SYMP.2019.8645980&partnerID=40&md5=6d0d55814771a4a69014332f019d5392

Languages: English-Great Britain (EN-GB)

View in Web of Science | View on publisher site | View citing articles in Web of Science

Abstract

We propose a spatio-temporal model for predicting anomaly price movements in the Stock Exchange of Thailand (SET). The model used a deep neural network classification algorithm that has a time series of of limit order books (LOB) as an input. There were three output classes: anomaly price uptrend, anomaly price downtrend, and normal price movements. We performed experiments to compare the efficiency among convolutional neural network model, Long short-term memory model, and the combination of both in order to classify anomaly price movements. The results of the experiment showed that the combination of both convolutional layers and Long short-term memory model had the highest accuracy with 74.55% for predicting abnormal price movements. ฉ 2019 IEEE.

Keywords

Anomaly Detection, Deep Neural Network, spatiotemporal model