Mark Leung - Academia.edu (original) (raw)
Papers by Mark Leung
Social Science Research Network, Aug 8, 2006
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Review of Pacific Basin Financial Markets and Policies, Sep 1, 1998
The difficulty in predicting exchange rates has been a long-standing problem in international fin... more The difficulty in predicting exchange rates has been a long-standing problem in international finance as most standard econometric methods are unable to produce significantly better forecasts than the random walk model. Recent studies provide some evidence for the ability of multivariate time-series models to generate better forecasts. At the same time, artificial neural network models have been emerging as alternatives to predict exchange rates. In this paper we propose a nonlinear forecast model combining the neural network with the multivariate econometric framework. This hybrid model contains two forecasting stages. A time series approach based on Bayesian Vector Autoregression (BVAR) models is applied to the first stage of forecasting. The estimates from BVAR are then used by the nonparametric General Regression Neural Network (GRNN) to generate enhanced forecasts. To evaluate the economic impact of forecasts, we develop a set of currency trading rules guided by these models. The optimal conditions implied by the investment rules maximize the expected profits given the expected changes in exchange rates and the interest rate differentials between domestic and foreign countries. Both empirical and simulation experiments suggest that the proposed nonlinear adaptive forecasting model not only produces better forecasts but also results in higher investment returns than other types of models. The effect of risk aversion is also considered in the investment simulation.
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Emerald Group Publishing Limited eBooks, 2013
This study examines the scheduling problem for a two-stage flowshop. All jobs are immediately ava... more This study examines the scheduling problem for a two-stage flowshop. All jobs are immediately available for processing and job characteristics including the processing times and due dates are known and certain. The goals of the scheduling problem are (1) to minimize the total flowtime for all jobs, (2) to minimize the total number of tardy jobs, and (3) to minimize both the total flowtime and the total number of tardy jobs simultaneously. Lower bound performances with respect to the total flowtime and the total number of tardy jobs are presented. Subsequently, this study identifies the special structure of schedules with minimum flowtime and minimum number of tardy jobs and develops three sets of heuristics which generate a Pareto set of bicriteria schedules. For each heuristic procedure, there are four options available for schedule generation. In addition, we provide enhancements to a variety of lower bounds with respect to flowtime and number of tardy jobs in a flowshop environment. Proofs and discussions to lower bound results are also included.
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Computers & Operations Research, Jun 1, 2004
Predicting exchange rates has long been a concern in international finance as most standard econo... more Predicting exchange rates has long been a concern in international finance as most standard econometric methods are unable to produce significantly better forecasts than the random walk model. Recent studies provide some evidence for the ability of using multivariate time series models to generate better forecasts. At the same time, artificial neural networks have been emerging as alternatives to predict
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IEEE Internet of Things Journal, 2022
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Decision Sciences, Sep 1, 1998
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Application of neural networks to an emerging $nancial
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Neural Computing and Applications
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Review of Pacific Basin Financial Markets and Policies, 1998
The difficulty in predicting exchange rates has been a long-standing problem in international fin... more The difficulty in predicting exchange rates has been a long-standing problem in international finance as most standard econometric methods are unable to produce significantly better forecasts than the random walk model. Recent studies provide some evidence for the ability of multivariate time-series models to generate better forecasts. At the same time, artificial neural network models have been emerging as alternatives to predict exchange rates. In this paper we propose a nonlinear forecast model combining the neural network with the multivariate econometric framework. This hybrid model contains two forecasting stages. A time series approach based on Bayesian Vector Autoregression (BVAR) models is applied to the first stage of forecasting. The estimates from BVAR are then used by the nonparametric General Regression Neural Network (GRNN) to generate enhanced forecasts. To evaluate the economic impact of forecasts, we develop a set of currency trading rules guided by these models. T...
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Decision Sciences, 1998
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Social Science Research Network, Aug 8, 2006
Bookmarks Related papers MentionsView impact
Review of Pacific Basin Financial Markets and Policies, Sep 1, 1998
The difficulty in predicting exchange rates has been a long-standing problem in international fin... more The difficulty in predicting exchange rates has been a long-standing problem in international finance as most standard econometric methods are unable to produce significantly better forecasts than the random walk model. Recent studies provide some evidence for the ability of multivariate time-series models to generate better forecasts. At the same time, artificial neural network models have been emerging as alternatives to predict exchange rates. In this paper we propose a nonlinear forecast model combining the neural network with the multivariate econometric framework. This hybrid model contains two forecasting stages. A time series approach based on Bayesian Vector Autoregression (BVAR) models is applied to the first stage of forecasting. The estimates from BVAR are then used by the nonparametric General Regression Neural Network (GRNN) to generate enhanced forecasts. To evaluate the economic impact of forecasts, we develop a set of currency trading rules guided by these models. The optimal conditions implied by the investment rules maximize the expected profits given the expected changes in exchange rates and the interest rate differentials between domestic and foreign countries. Both empirical and simulation experiments suggest that the proposed nonlinear adaptive forecasting model not only produces better forecasts but also results in higher investment returns than other types of models. The effect of risk aversion is also considered in the investment simulation.
Bookmarks Related papers MentionsView impact
Emerald Group Publishing Limited eBooks, 2013
This study examines the scheduling problem for a two-stage flowshop. All jobs are immediately ava... more This study examines the scheduling problem for a two-stage flowshop. All jobs are immediately available for processing and job characteristics including the processing times and due dates are known and certain. The goals of the scheduling problem are (1) to minimize the total flowtime for all jobs, (2) to minimize the total number of tardy jobs, and (3) to minimize both the total flowtime and the total number of tardy jobs simultaneously. Lower bound performances with respect to the total flowtime and the total number of tardy jobs are presented. Subsequently, this study identifies the special structure of schedules with minimum flowtime and minimum number of tardy jobs and develops three sets of heuristics which generate a Pareto set of bicriteria schedules. For each heuristic procedure, there are four options available for schedule generation. In addition, we provide enhancements to a variety of lower bounds with respect to flowtime and number of tardy jobs in a flowshop environment. Proofs and discussions to lower bound results are also included.
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Computers & Operations Research, Jun 1, 2004
Predicting exchange rates has long been a concern in international finance as most standard econo... more Predicting exchange rates has long been a concern in international finance as most standard econometric methods are unable to produce significantly better forecasts than the random walk model. Recent studies provide some evidence for the ability of using multivariate time series models to generate better forecasts. At the same time, artificial neural networks have been emerging as alternatives to predict
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IEEE Internet of Things Journal, 2022
Bookmarks Related papers MentionsView impact
Decision Sciences, Sep 1, 1998
Bookmarks Related papers MentionsView impact
Application of neural networks to an emerging $nancial
Bookmarks Related papers MentionsView impact
Neural Computing and Applications
Bookmarks Related papers MentionsView impact
Review of Pacific Basin Financial Markets and Policies, 1998
The difficulty in predicting exchange rates has been a long-standing problem in international fin... more The difficulty in predicting exchange rates has been a long-standing problem in international finance as most standard econometric methods are unable to produce significantly better forecasts than the random walk model. Recent studies provide some evidence for the ability of multivariate time-series models to generate better forecasts. At the same time, artificial neural network models have been emerging as alternatives to predict exchange rates. In this paper we propose a nonlinear forecast model combining the neural network with the multivariate econometric framework. This hybrid model contains two forecasting stages. A time series approach based on Bayesian Vector Autoregression (BVAR) models is applied to the first stage of forecasting. The estimates from BVAR are then used by the nonparametric General Regression Neural Network (GRNN) to generate enhanced forecasts. To evaluate the economic impact of forecasts, we develop a set of currency trading rules guided by these models. T...
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Decision Sciences, 1998
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