Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

Consumer use intention of online financial products:the Yuebao example

Drawing on a valence framework and innovation diffusion theory,this study examines Yuebao deployment to model consumers’intention to use financial products offered online.We collect data via an online survey among a young age bracket and use a VisualPLS graphic interface to test our model.The results indicate that first,compatibility and advantages in relative utility significantly enhance consumers’use intentions and that ease of use has an indirect influence.Second,the negative utility of perceived risk no longer significantly affects intention to use.This mainly lies in consumers’perceived risk focused on the security of online financial products,which has become a part of all online transactions.Third,trust indirectly affects intention to use by influencing utility.

Research on the Influencing Factors of Purchasing Internet Financial Wealth Management Products Based on AHP

Internet financial wealth management product(IFWMP)has recently been one of the most gained popularity.There are limited quantitative research on IFWMP which can help customers to choose products based on the significance of each factor.In this paper,a multi-criteria decision-making model for IFWMP was developed,namely the analytic hierarchy process(AHP)which is commonly used to make decisions for unstructured problems through quantifying weights of each criterion.This paper investigated ten influential factors relevant to the purchase of IFWMP and analyzed the frequency of collected response to show the significance of the factors.Based on the quantified weights,the results of the research indicated that compatibility,product liquidity,perceived ease of use,and perceived usefulness affected investors purchasing behaviors and that every investor should pay great attention to.

Research on the Influencing Factors of Purchasing Internet Financial Wealth Management Products based on AHP

Internet financial wealth management product(IFWMP)has recently been one of the most popularity.There is limited quantitative research on IFWMP which can helps customers to choose the products based on the significance of each factor.In the paper,a multi-criteria decision-making model of IFWMP was developed,namely analytic hierarchy process(AHP)which is used to make decisions to the unstructured problems through quantifying weights of each criterion.This paper investigated ten influential factors relevant to the purchase of IFWMP and analyzed the frequency of collected responds to show the significance of factors.Based on the quantified weights,the result of the research indicated that compatibility,product liquidity,perceived ease of use and perceived usefulness affect the investors purchasing behaviors most that every investors should pay great attention to.

Investigating the components of fintech ecosystem for distributed energy investments with an integrated quantum spherical decision support system

This study aimed to evaluate the components of a fintech ecosystem for distributed energy investments.A new decision-making model was created using multiple stepwise weight assessment ratio analysis and elimination and choice translating reality techniques based on quantum spherical fuzzy sets.First,in this model,the criteria for distributed energy investment necessities were weighted.Second,we ranked the components of the fintech ecosystem for distributed energy investments.The main contribution of this study is that appropriate strategies can be presented to design effective fintech ecosystems to increase distributed energy investments,by considering an original fuzzy decision-making model.Capacity is the most critical issue with respect to distributed energy investment necessities because it has the greatest weight(0.261).Pricing is another significant factor for this condition,with a weight of 0.254.Results of the ranking of the components of the fintech ecosystem indicate that end users are of the greatest importance for the effectiveness of this system.It is necessary to develop new techniques for the energy storage process,especially with technological developments,to prevent disruptions in energy production capacity.In addition,customers’expectations should be considered for the development of effective and user-friendly financial products that are preferred by a wider audience.This would have a positive effect on fintech ecosystem performance.