An Improved Deep-Learning-Based Financial Market Forecasting Model in the Digital Economy

Yang Dexiang, Mu Shengdong, Yunjie Liu (), Gu Jijian and Lien Chaolung
Additional contact information
Yang Dexiang: School of Finance, Central University of Finance and Economics, 39, South College Road, Beijing 100081, China
Mu Shengdong: Collaborative Innovation Center of Green Development in the Wuling Shan Region, Yangtze Normal University, Chongqing 408100, China
Yunjie Liu: Fudan Postdoctoral Fellowships in Applied Economic Studies, Fudan University, Shanghai 200433, China
Gu Jijian: Chongqing Vocational College of Transportation Jiangjin, Chongqing 402200, China
Lien Chaolung: International College, Krirk University, Bangkok 10220, Thailand

Mathematics, 2023, vol. 11, issue 6, 1-18

Abstract: The high-complexity, high-reward, and high-risk characteristics of financial markets make them an important and interesting study area. Elliott’s wave theory describes the changing models of financial markets categorically in terms of wave models and is an advanced feature representation of financial time series. Meanwhile, deep learning is a breakthrough technique for nonlinear intelligent models, which aims to discover advanced feature representations of data and thus obtain the intrinsic laws underlying the data. This study proposes an innovative combination of these two concepts to create a deep learning + Elliott wave principle (DL-EWP) model. This model achieves the prediction of future market movements by extracting and classifying Elliott wave models from financial time series. The model’s effectiveness is empirically validated by running it on financial data from three major markets and comparing the results with those of the SAE, MLP, BP network, PCA-BP, and SVD-BP models. Interestingly, the DL-EWP model based on deep confidence networks outperforms other models in terms of stability, convergence speed, and accuracy and has a higher forecasting performance. Thus, the DL-EWP model can improve the accuracy of financial forecasting models that incorporate Elliott’s wave theory.

Keywords: deep learning; financial forecasting; digital economy; Elliott’s wave theory (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/6/1466/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/6/1466/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:6:p:1466-:d:1100693

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().