Mark Leung - Academia.edu (original) (raw)

Papers by Mark Leung

Research paper thumbnail of Regression Neural Network for Error Correction in Foreign Exchange Forecasting and Trading

Social Science Research Network, Aug 8, 2006

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Research paper thumbnail of Dynamic Foreign Currency Trading Guided by Adaptive Forecasting

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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Research paper thumbnail of Predicting the Performance of a Two-Stage Flowshop: Lower Bounds and Heuristics for Single and Bicriteria Measures

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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Research paper thumbnail of Regression neural network for error correction in foreign exchange forecasting and trading

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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Research paper thumbnail of PUF-Based Authentication and Key Agreement Protocols for IoT, WSNs, and Smart Grids: A Comprehensive Survey

IEEE Internet of Things Journal, 2022

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Research paper thumbnail of Customer Order Scheduling in a General Job Shop Environment

Decision Sciences, Sep 1, 1998

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Research paper thumbnail of market: forecasting and trading the Taiwan Stock Index

Application of neural networks to an emerging $nancial

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Research paper thumbnail of Financial hedging in energy market by cross-learning machines

Neural Computing and Applications

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Research paper thumbnail of Dynamic Foreign Currency Trading Guided by Adaptive Forecasting

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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Research paper thumbnail of Customer Order Scheduling in a General Job Shop Environment

Decision Sciences, 1998

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Research paper thumbnail of Methods and Devices for Cryogenic Carotid Body Ablation

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Research paper thumbnail of Endovascular Catheters and Methods for Carotid Body Ablation

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Research paper thumbnail of Percutaneous methods and devices for carotid body ablation

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Research paper thumbnail of Cryoablation apparatuses, systems, and methods for renal neuromodulation

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Research paper thumbnail of Catheters Having Tethered Neuromodulation Units and Associated Devices, Systems, and Methods

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Research paper thumbnail of Devices, Systems and Methods for Evaluation and Feedback of Neuromodulation Treatment

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Research paper thumbnail of Catheter Apparatuses, Systems, and Methods for Renal Neuromodulation

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Research paper thumbnail of Multi-Directional Deflectable Catheter Apparatuses, Systems, and Methods for Renal Neuromodulation

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Research paper thumbnail of Devices and Systems for Carotid Body Ablation

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Research paper thumbnail of Neuromodulation cryotherapeutic devices and associated systems and methods

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Research paper thumbnail of Regression Neural Network for Error Correction in Foreign Exchange Forecasting and Trading

Social Science Research Network, Aug 8, 2006

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Dynamic Foreign Currency Trading Guided by Adaptive Forecasting

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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Research paper thumbnail of Predicting the Performance of a Two-Stage Flowshop: Lower Bounds and Heuristics for Single and Bicriteria Measures

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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Research paper thumbnail of Regression neural network for error correction in foreign exchange forecasting and trading

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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Research paper thumbnail of PUF-Based Authentication and Key Agreement Protocols for IoT, WSNs, and Smart Grids: A Comprehensive Survey

IEEE Internet of Things Journal, 2022

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Research paper thumbnail of Customer Order Scheduling in a General Job Shop Environment

Decision Sciences, Sep 1, 1998

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Research paper thumbnail of market: forecasting and trading the Taiwan Stock Index

Application of neural networks to an emerging $nancial

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Financial hedging in energy market by cross-learning machines

Neural Computing and Applications

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Dynamic Foreign Currency Trading Guided by Adaptive Forecasting

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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Research paper thumbnail of Customer Order Scheduling in a General Job Shop Environment

Decision Sciences, 1998

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Research paper thumbnail of Methods and Devices for Cryogenic Carotid Body Ablation

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Endovascular Catheters and Methods for Carotid Body Ablation

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Percutaneous methods and devices for carotid body ablation

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Cryoablation apparatuses, systems, and methods for renal neuromodulation

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Catheters Having Tethered Neuromodulation Units and Associated Devices, Systems, and Methods

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Devices, Systems and Methods for Evaluation and Feedback of Neuromodulation Treatment

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Catheter Apparatuses, Systems, and Methods for Renal Neuromodulation

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Multi-Directional Deflectable Catheter Apparatuses, Systems, and Methods for Renal Neuromodulation

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Devices and Systems for Carotid Body Ablation

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Neuromodulation cryotherapeutic devices and associated systems and methods

Bookmarks Related papers MentionsView impact