Anomalies,which are incompatible with the efficient market hypothesis and mean a deviation from normality,have attracted the attention of both financial investors and researchers.A salient research topic is the existe...Anomalies,which are incompatible with the efficient market hypothesis and mean a deviation from normality,have attracted the attention of both financial investors and researchers.A salient research topic is the existence of anomalies in cryptocurrencies,which have a different financial structure from that of traditional financial markets.This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market,which is hard to predict.It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods.An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies.On October 6,2021,Bitcoin(BTC),Ethereum(ETH),and Cardano(ADA),which are the top three cryptocurrencies in terms of market value,were selected for this study.The data for the analysis,consisting of the daily closing prices for BTC,ETH,and ADA,were obtained from the Coinmarket.com website from January 1,2018 to May 31,2022.The effectiveness of the established models was tested with mean squared error,root mean squared error,mean absolute error,and Theil’s U1,and R2 OOS was used for out-of-sample.The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models.When the models created with feedforward artificial neural networks are examined,the existence of the day-of-the-week anomaly is established for BTC,but no day-of-the-week anomaly for ETH and ADA was found.展开更多
Inspired by Cardano＇s method for solving cubic scalar equations, the addi- tive decomposition of spherical/deviatoric tensor （DSDT） is revisited from a new view- point. This decomposition simplifies the cubic tenso...Inspired by Cardano＇s method for solving cubic scalar equations, the addi- tive decomposition of spherical/deviatoric tensor （DSDT） is revisited from a new view- point. This decomposition simplifies the cubic tensor equation, decouples the spher- ical/deviatoric strain energy density, and lays the foundation for the von Mises yield criterion. Besides, it is verified that under the precondition of energy decoupling and the simplest form, the DSDT is the only possible form of the additive decomposition with physical meanings.展开更多
Electrostatic Rayleigh-Taylor (ERT) mode/instability is studied in a non-uni-form quantum magnetoplasma, whose constituents are electrons and positrons with fraction of ions. The effects of quantum corrections (i.e. B...Electrostatic Rayleigh-Taylor (ERT) mode/instability is studied in a non-uni-form quantum magnetoplasma, whose constituents are electrons and positrons with fraction of ions. The effects of quantum corrections (i.e. Bohm potential and temperature degeneracy) and magnetic field on ERT mode are investigated with astrophysical plasma application. A generalized dispersion relation is deduced under the drift wave approximation. The presence of positron makes the dispersion relation a cubic equation. Different roots of both real and imaginary parts of the RT mode are examined by applying the Cardano’s method of solving the cubic equation. The dispersion relation and the growth rates of RT instability are examined both analytically and numerically with effects of electron and positron density, and magnetic field variations. It is shown that the magnetic field and positron density have stabilizing effectuates on ERT mode while due to electron density the mode becomes unstable. The present work is antici-pated to be of physical relevance in the studies of laboratory laser-produced plasmas as well as in the study of compact magnetized astrophysical objects like white dwarfs.展开更多
基金Financial support.There is no sponsorship.The publication of study results is not contingent on the sponsor’s approval or censorship of the manuscript.
文摘Anomalies,which are incompatible with the efficient market hypothesis and mean a deviation from normality,have attracted the attention of both financial investors and researchers.A salient research topic is the existence of anomalies in cryptocurrencies,which have a different financial structure from that of traditional financial markets.This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market,which is hard to predict.It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods.An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies.On October 6,2021,Bitcoin(BTC),Ethereum(ETH),and Cardano(ADA),which are the top three cryptocurrencies in terms of market value,were selected for this study.The data for the analysis,consisting of the daily closing prices for BTC,ETH,and ADA,were obtained from the Coinmarket.com website from January 1,2018 to May 31,2022.The effectiveness of the established models was tested with mean squared error,root mean squared error,mean absolute error,and Theil’s U1,and R2 OOS was used for out-of-sample.The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models.When the models created with feedforward artificial neural networks are examined,the existence of the day-of-the-week anomaly is established for BTC,but no day-of-the-week anomaly for ETH and ADA was found.
基金supported by the National Natural Science Foundation of China(Nos.11072125 and11272175)the Specialized Research Fund for the Doctoral Program of Higher Education of China(No.20130002110044)the China Postdoctoral Science Foundation(No.2015M570035)
文摘Inspired by Cardano＇s method for solving cubic scalar equations, the addi- tive decomposition of spherical/deviatoric tensor （DSDT） is revisited from a new view- point. This decomposition simplifies the cubic tensor equation, decouples the spher- ical/deviatoric strain energy density, and lays the foundation for the von Mises yield criterion. Besides, it is verified that under the precondition of energy decoupling and the simplest form, the DSDT is the only possible form of the additive decomposition with physical meanings.
文摘Electrostatic Rayleigh-Taylor (ERT) mode/instability is studied in a non-uni-form quantum magnetoplasma, whose constituents are electrons and positrons with fraction of ions. The effects of quantum corrections (i.e. Bohm potential and temperature degeneracy) and magnetic field on ERT mode are investigated with astrophysical plasma application. A generalized dispersion relation is deduced under the drift wave approximation. The presence of positron makes the dispersion relation a cubic equation. Different roots of both real and imaginary parts of the RT mode are examined by applying the Cardano’s method of solving the cubic equation. The dispersion relation and the growth rates of RT instability are examined both analytically and numerically with effects of electron and positron density, and magnetic field variations. It is shown that the magnetic field and positron density have stabilizing effectuates on ERT mode while due to electron density the mode becomes unstable. The present work is antici-pated to be of physical relevance in the studies of laboratory laser-produced plasmas as well as in the study of compact magnetized astrophysical objects like white dwarfs.
