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.
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.
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
The primary objective of this study is to examine the performance of order-based dispatching rule... more The primary objective of this study is to examine the performance of order-based dispatching rules in a general job shop, where the environmental factors are shop utilization and due date tightness. An order is defined as a collection of jobs that are shipped as a g r o u p a n order-to the customer, only on completion of the last job of the order. We specifically compare dispatching rules from past job-based studies to some rules adapted to encompass order characteristics. Standard flow time and tardiness measures are used, but in addition, we introduce measures that combine average performance with variation in an attempt to capture the performance of a majority of the orders processed in the shop. Of the 16 dispatching ruies tested, our results show that four of the simple rules dominate the others. We also found that order-based rules perform better than their job-based counterparts. The study makes use of multivariate statistical analysis, in addition to the usual univariate tests, which can provide additional insight to managers using multiple criteria in their decision process.
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...
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.
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.
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
The primary objective of this study is to examine the performance of order-based dispatching rule... more The primary objective of this study is to examine the performance of order-based dispatching rules in a general job shop, where the environmental factors are shop utilization and due date tightness. An order is defined as a collection of jobs that are shipped as a g r o u p a n order-to the customer, only on completion of the last job of the order. We specifically compare dispatching rules from past job-based studies to some rules adapted to encompass order characteristics. Standard flow time and tardiness measures are used, but in addition, we introduce measures that combine average performance with variation in an attempt to capture the performance of a majority of the orders processed in the shop. Of the 16 dispatching ruies tested, our results show that four of the simple rules dominate the others. We also found that order-based rules perform better than their job-based counterparts. The study makes use of multivariate statistical analysis, in addition to the usual univariate tests, which can provide additional insight to managers using multiple criteria in their decision process.
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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