You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

AComNN: Attention enhanced Compound Neural Network for financial time-series forecasting with cross-regional features

Authors:

Zhen Yang , Jacky Keung , Md Alamgir Kabir , Xiao Yu , Yutian Tang , Miao Zhang , Shuo Feng

Publication Type:

Journal article

Venue:

Applied Soft Computing

Abstract

In recent years, many works spring out to adopt the forecast-based approach to support the investment decision in the financial market. Nevertheless, most of them do not consider mining the hidden patterns in the cross-regional financial time-series. However, the fluctuation in financial markets has always been affected by the global economy, instead of a single market. To overcome this issue, this article proposes an Attention enhanced Compound Neural Network (AComNN) that can be applied on features of multiple-sources, including different financial markets and economic entities. The proposed novel approach compounds of Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and self-attention to progressively capture the time-zone-dependent context behind the financial time-series across regions with multiple filters. Thereby, it provides trading signals for supporting the financial investment decision. The proposed AComNN has been applied on the Hong Kong Hang Seng Index (HSI) trend prediction based on various initial features across regions. The experimental result demonstrates that the AComNN achieves the highest average accuracy for the one-day ahead trend prediction over 60%. Besides, it reveals highly superior competitiveness on the forecasting capability improved by 13.36% on average compared with the baselines. Therefore, we encourage to adopt the proposed method to the practitioners and provide a new thought, considering the analysis of cross-regional features, in the financial time-series forecasting.

Bibtex

@article{Yang6536,
author = {Zhen Yang and Jacky Keung and Md Alamgir Kabir and Xiao Yu and Yutian Tang and Miao Zhang and Shuo Feng},
title = {AComNN: Attention enhanced Compound Neural Network for financial time-series forecasting with cross-regional features},
volume = {113},
month = {July},
year = {2021},
journal = {Applied Soft Computing},
url = {http://www.es.mdu.se/publications/6536-}
}