Multi-scale Two-way Deep Neural Network for Stock Trend Prediction release_pucrzozhavb6vaiknmtjcjie3u

by Guang Liu, Yuzhao Mao, Qi Sun, Hailong Huang, Weiguo Gao, Xuan Li, Jianping Shen, Ruifan Li, Xiaojie Wang

Published in International Joint Conference on Artificial Intelligence by International Joint Conferences on Artificial Intelligence Organization.

2020   p4506-4512

Abstract

Stock Trend Prediction(STP) has drawn wide attention from various fields, especially Artificial Intelligence. Most previous studies are single-scale oriented which results in information loss from a multi-scale perspective. In fact, multi-scale behavior is vital for making intelligent investment decisions. A mature investor will thoroughly investigate the state of a stock market at various time scales. To automatically learn the multi-scale information in stock data, we propose a Multi-scale Two-way Deep Neural Network. It learns multi-scale patterns from two types of scale-information, wavelet-based and downsampling-based, by eXtreme Gradient Boosting and Recurrent Convolutional Neural Network, respectively. After combining the learned patterns from the two-way, our model achieves state-of-the-art performance on FI-2010 and CSI-2016, where the latter is our published long-range stock dataset to help future studies for STP task. Extensive experimental results on the two datasets indicate that multi-scale information can significantly improve the STP performance and our model is superior in capturing such information.
In application/xml+jats format

Archived Files and Locations

application/pdf  370.9 kB
file_h33zet7d3zde7fiuc6wl65qd7y
www.ijcai.org (web)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  paper-conference
Stage   published
Year   2020
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: ecabbcfc-c823-4218-8847-5b21423b25ab
API URL: JSON