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| Title: | Simple ensemble-averaging model based on generalized regression neural network in financial forecasting problems | |
| Author (s): | Dagli, Cihan H. Disorntetiwat, P. | |
| Department/Lab Affiliations: | Engineering Management & Systems Engineering Smart Engineering Systems Lab | |
| Keywords: | 500 index S&P accuracy asset price movement currency exchange rate financial forecasting problems forecasting theory generalized regression neural network neural nets percent direction correctness predictive abilities root mean squared error simple ensemble-averaging model standard deviation stock markets | |
| Issue Date: | 2000 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Abstract: | Introduces an ensemble-averaging model based on a GRNN (generalized regression neural network) for financial forecasting. The model trains all input individually using GRNNs and uses a simple ensemble-averaging committee machine to improve the accuracy performance. In a financial problem, there are many different factors that can effect the asset price movement at different times. An experiment is implemented in two different data sets, S&P 500 index and currency exchange rate. The predictive abilities of the model are evaluated on the basis of root mean squared error, standard deviation and percent direction correctness. The study shows a promising result of the model in both data sets. | |
| Print Status: | Final version | |
| Type: | Article - Conference proceedings text | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
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| title | Simple ensemble-averaging model based on generalized regression neural network in financial forecasting problems | |
| contributor.author | Dagli, Cihan H. | |
| contributor.author | Disorntetiwat, P. | |
| contributor.deptlab | Engineering Management & Systems Engineering | |
| contributor.deptlab | Smart Engineering Systems Lab | |
| subject | 500 index | |
| subject | S&P | |
| subject | accuracy | |
| subject | asset price movement | |
| subject | currency exchange rate | |
| subject | financial forecasting problems | |
| subject | forecasting theory | |
| subject | generalized regression neural network | |
| subject | neural nets | |
| subject | percent direction correctness | |
| subject | predictive abilities | |
| subject | root mean squared error | |
| subject | simple ensemble-averaging model | |
| subject | standard deviation | |
| subject | stock markets | |
| date.issued | 2000 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Disorntetiwat, P.; Dagli, C.H., "Simple ensemble-averaging model based on generalized regression neural network in financial forecasting problems," The IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, 2000 AS-SPCC, 2000 Pages:477-480 | |
| identifier.pub.URI | ||
| description.abstract | Introduces an ensemble-averaging model based on a GRNN (generalized regression neural network) for financial forecasting. The model trains all input individually using GRNNs and uses a simple ensemble-averaging committee machine to improve the accuracy performance. In a financial problem, there are many different factors that can effect the asset price movement at different times. An experiment is implemented in two different data sets, S&P 500 index and currency exchange rate. The predictive abilities of the model are evaluated on the basis of root mean squared error, standard deviation and percent direction correctness. The study shows a promising result of the model in both data sets. | |
| type | Article - Conference proceedings | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| date.accessioned | 2007-04-05T14:09:01Z | |
| date.available | 2007-04-05T14:09:00Z | |
| identifier.persist.URI | ||
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