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...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.展开更多
Employing the Differential Dynamics Method, a nonlinear dynamic model is set up to describe the international financial crises contagion within a short time between two countries. The two countries’ control force dep...Employing the Differential Dynamics Method, a nonlinear dynamic model is set up to describe the international financial crises contagion within a short time between two countries. The two countries’ control force depending on the timely financial assistance, the positive attitude and actions to rescue other infected countries, and investor confidence aggregation, and the immunity ability of the infected country are considered as the major reasons to drive the nonlinear fluctuations of the stock return rates in both countries during the crisis. According to the Ordinary Differential Equations Qualitative Theory, we found that there are three cases of financial crises contagion within a brief time between two countries: weak contagion with instability but inhibition, contagion with limit and controllable oscillation, and strong contagion without control in a brief time.展开更多
基金supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493)by the NRF grant funded by the Korea government(MSIT)(NRF-2022R1A2C1004401).
文摘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.
文摘Employing the Differential Dynamics Method, a nonlinear dynamic model is set up to describe the international financial crises contagion within a short time between two countries. The two countries’ control force depending on the timely financial assistance, the positive attitude and actions to rescue other infected countries, and investor confidence aggregation, and the immunity ability of the infected country are considered as the major reasons to drive the nonlinear fluctuations of the stock return rates in both countries during the crisis. According to the Ordinary Differential Equations Qualitative Theory, we found that there are three cases of financial crises contagion within a brief time between two countries: weak contagion with instability but inhibition, contagion with limit and controllable oscillation, and strong contagion without control in a brief time.
