Reference Hub

This research has been cited in:

Article
A qualitatively analyzable two-stage ensemble model based on machine learning for credit risk early warning: Evidence from Chinese manufacturing companiesInformation Processing & Management10.1016/j.ipm.2023.103267
Article
The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big DataIEEE Access10.1109/ACCESS.2024.3351468
Article
The Trust of Electronic Service Quality in Customer Patronage Willingness: The Regulatory Effect of Adopting Advanced TechnologyIEEE Transactions on Engineering Management10.1109/TEM.2023.3346926
Article
Convolutional neural networks applied to data organized as OLAP cubesExpert Systems10.1111/exsy.13454
Article
The Analysis of Music Emotion and Visualization Fusing Long Short-Term Memory Networks Under the Internet of ThingsIEEE Access10.1109/ACCESS.2023.3341926
Article
Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of ThingsIEEE Access10.1109/ACCESS.2023.3321303
Article
Application of Big Data Technology in Enterprise Information Security Management and Risk AssessmentJournal of Global Information Management10.4018/JGIM.324465
Article
Impacts of Word of Mouth (WOM) on E-Business Online PricingJournal of Global Information Management10.4018/JGIM.324813
Article
Deep Reinforcement Learning for Adaptive Stock TradingJournal of Organizational and End User Computing10.4018/JOEUC.335083
Article
The Social Media Big Data Analysis for Demand Forecasting in the Context of GlobalizationJournal of Organizational and End User Computing10.4018/JOEUC.325217
Article
Enterprise Collaboration Optimization in China Based on Supply Chain Resilience EnhancementInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.331400
Article
The Application of Big Data and Artificial Intelligence Technology in Enterprise Information Security Management and Risk AssessmentJournal of Organizational and End User Computing10.4018/JOEUC.326934
Article
The Analysis of Instrument Automatic Monitoring and Control Systems Under Artificial IntelligenceInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.336844
Article
The Use of the Analytic Hierarchy Process in Improving Psychological Empowerment and Employee PerformanceJournal of Organizational and End User Computing10.4018/JOEUC.321171
Article
Research on Financial Risk Intelligent Monitoring and Early Warning Model Based on LSTM, Transformer, and Deep LearningJournal of Organizational and End User Computing10.4018/JOEUC.337607

Application of Improved Convolution Neural Network in Financial Forecasting

Wensheng Dai

Source Title: Journal of Organizational and End User Computing (JOEUC)34(3)

ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781799893264|DOI: 10.4018/JOEUC.289222

Cite Article Cite Article

MLA

Dai, Wensheng. "Application of Improved Convolution Neural Network in Financial Forecasting." JOEUC vol.34, no.3 2022: pp.1-16. http://doi.org/10.4018/JOEUC.289222

APA

Dai, W. (2022). Application of Improved Convolution Neural Network in Financial Forecasting. Journal of Organizational and End User Computing (JOEUC), 34(3), 1-16. http://doi.org/10.4018/JOEUC.289222

Chicago

Dai, Wensheng. "Application of Improved Convolution Neural Network in Financial Forecasting," Journal of Organizational and End User Computing (JOEUC) 34, no.3: 1-16. http://doi.org/10.4018/JOEUC.289222

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

Financial status and its role in the national economy have been increasingly recognized. In order to deduce the source of monetary funds and determine their whereabouts, financial information and prediction have become a scientific method that can not be ignored in the development of national economy. This paper improves the existing CNN and applies it to financial credit from different perspectives. Firstly, the noise of the collected data set is deleted, and then the clustering result is more stable by principal component analysis. The observation vectors are segmented to obtain a set of observation vectors corresponding to each hidden state. Based on the output of PCA algorithm, we recalculate the mean and variance of all kinds of observation vectors, and use the new mean and covariance matrix as credit financial credit, and then determine the best model parameters.The empirical results based on specific data from China's stock market show that the improved convolutional neural network proposed in this paper has advantages and the prediction accuracy reaches.