IFSO-ECNN: Digital economy of sudden financial disaster and emergency risk avoidance

IFSO-ECNN: Digital economy of sudden financial disaster and emergency risk avoidance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article IFSO-ECNN: Digital economy of sudden financial disaster and emergency risk avoidance Xiaolei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7073864/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Due to the fast growth and development of the digital economy, the way in which money is exchanged and transacted has changed drastically. However, this change also poses a previously unseen threat to the economy, particularly during periods of unexpected financial disasters. Complex emergency risk avoidance tactics are absolutely necessary in these kinds of scenarios. This project aims to construct a robust framework that can forecast and mitigate the impact of sudden financial crises on the digital economy using an intelligent fish swarm-optimized ensemble convolution neural network [IFSO-ECNN]. The proposed Deep Learning (DL) approach constructs prediction models by integrating relevant socioeconomic factors, market indicators, and a wealth of historical financial data. The financial data was prepared by applying min-max normalisation. The features were extracted from the preprocessed data using kernel-principal component analysis (K-PCA). This study adds to our knowledge of how IFSO-ECNN might help the digital economy better prepare for and recover from unexpected financial catastrophes. Using both historical and real-time data, the suggested framework shows that it is possible to make smart judgements and proactively reduce financial risks. This study provides policymakers, financial institutions, and enterprises with useful insights for developing more resilient strategies in the face of unforeseen economic shocks by integrating modern IFSO-ECNN methodologies with domain experience. Digital Economy Financial Disasters Intelligent Fish Swarm Optimized Ensemble Convolution Neural Network (IFSO-ECNN) Kernel-Principal Component Analysis (K-PCA) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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