Tank sloshing in ship cargo is excited by ship motions, which induces impact load on tank wall and then affects the ship motion. Wave forces acting on ship hull and the retardation function are solved by using three-d...Tank sloshing in ship cargo is excited by ship motions, which induces impact load on tank wall and then affects the ship motion. Wave forces acting on ship hull and the retardation function are solved by using three-dimensional frequency domain theory and an impulse response function method based on the potential flow theory, and global ship motion is examined coupling with nonlinear tank sloshing which is simulated by viscous flow theory. Based on the open source Computational Fluid Dynamics （CFD） development platform Open Field Operation and Manipulation （OpenFOAM）, numerical calculation of ship motion coupled with tank sloshing is achieved and the corresponding numerical simulation and validation are carried out. With this method, the interactions of wave, ship body and tank sloshing are completely taken into consideration. This method has quite high efficiency for it takes advantage of potential flow theory for outer flow field and viscous flow theory for inside tank sloshing respectively. The numerical and experimental results of the ship motion agree well with each other.展开更多
Overconfidence behavior,one form of positive illusion,has drawn considerable attention throughout history because it is viewed as the main reason for many crises.Investors’overconfidence,which can be observed as over...Overconfidence behavior,one form of positive illusion,has drawn considerable attention throughout history because it is viewed as the main reason for many crises.Investors’overconfidence,which can be observed as overtrading following positive returns,may lead to inefficiencies in stock markets.To the best of our knowledge,this is the first study to examine the presence of investor overconfidence by employing an artificial intelligence technique and a nonlinear approach to impulse responses to analyze the impact of different return regimes on the overconfidence attitude.We examine whether investors in an emerging stock market(Borsa Istanbul)exhibit overconfidence behavior using a feed-forward,neural network,nonlinear Granger causality test and nonlinear impulseresponse functions based on local projections.These are the first applications in the relevant literature due to the novelty of these models in forecasting high-dimensional,multivariate time series.The results obtained from distinguishing between the different market regimes to analyze the responses of trading volume to return shocks contradict those in the literature,which is the key contribution of the study.The empirical findings imply that overconfidence behavior exhibits asymmetries in different return regimes and is persistent during the 20-day forecasting horizon.Overconfidence is more persistent in the low-than in the high-return regime.In the negative interest-rate period,a high-return regime induces overconfidence behavior,whereas in the positive interest-rate period,a low-return regime induces overconfidence behavior.Based on the empirical findings,investors should be aware that portfolio gains may result in losses depending on aggressive and excessive trading strategies,particularly in low-return regimes.展开更多
文摘Tank sloshing in ship cargo is excited by ship motions, which induces impact load on tank wall and then affects the ship motion. Wave forces acting on ship hull and the retardation function are solved by using three-dimensional frequency domain theory and an impulse response function method based on the potential flow theory, and global ship motion is examined coupling with nonlinear tank sloshing which is simulated by viscous flow theory. Based on the open source Computational Fluid Dynamics （CFD） development platform Open Field Operation and Manipulation （OpenFOAM）, numerical calculation of ship motion coupled with tank sloshing is achieved and the corresponding numerical simulation and validation are carried out. With this method, the interactions of wave, ship body and tank sloshing are completely taken into consideration. This method has quite high efficiency for it takes advantage of potential flow theory for outer flow field and viscous flow theory for inside tank sloshing respectively. The numerical and experimental results of the ship motion agree well with each other.
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文摘Overconfidence behavior,one form of positive illusion,has drawn considerable attention throughout history because it is viewed as the main reason for many crises.Investors’overconfidence,which can be observed as overtrading following positive returns,may lead to inefficiencies in stock markets.To the best of our knowledge,this is the first study to examine the presence of investor overconfidence by employing an artificial intelligence technique and a nonlinear approach to impulse responses to analyze the impact of different return regimes on the overconfidence attitude.We examine whether investors in an emerging stock market(Borsa Istanbul)exhibit overconfidence behavior using a feed-forward,neural network,nonlinear Granger causality test and nonlinear impulseresponse functions based on local projections.These are the first applications in the relevant literature due to the novelty of these models in forecasting high-dimensional,multivariate time series.The results obtained from distinguishing between the different market regimes to analyze the responses of trading volume to return shocks contradict those in the literature,which is the key contribution of the study.The empirical findings imply that overconfidence behavior exhibits asymmetries in different return regimes and is persistent during the 20-day forecasting horizon.Overconfidence is more persistent in the low-than in the high-return regime.In the negative interest-rate period,a high-return regime induces overconfidence behavior,whereas in the positive interest-rate period,a low-return regime induces overconfidence behavior.Based on the empirical findings,investors should be aware that portfolio gains may result in losses depending on aggressive and excessive trading strategies,particularly in low-return regimes.
