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| Title: | Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks | |
| Author (s): | Saad, E.W. Prokhorov, D.V. Wunsch, Donald C. | |
| Department/Lab Affiliations: | Applied Computational Intelligence Laboratory Electrical and Computer Engineering | |
| Keywords: | Kalman filters conjugate gradient methods conjugate gradient training daily closing price feedforward neural nets filtering theory forecasting theory learning (artificial intelligence) low false alarm multilayer perceptrons multistream extended Kalman filter training nonlinear filters option trading predictability analysis techniques probabilistic neural networks recurrent neural nets recurrent neural networks risk/reward ratio short-term trends stock markets stock trend prediction time delay neural networks time series | |
| Issue Date: | 1998 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Saad, E.W.; Prokhorov, D.V.; Wunsch, D.C., II, "Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks," IEEE Transactions on Neural Networks, vol.9, no.6 pp.1456-1470, Nov 1998 | |
| Abstract: | Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We also discuss different predictability analysis techniques and perform an analysis of predictability based on a history of daily closing price. Our results indicate that all the networks are feasible, the primary preference being one of convenience | |
| Print Status: | Final version | |
| Type: | Article - Journal 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 | Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks | |
| contributor.author | Saad, E.W. | |
| contributor.author | Prokhorov, D.V. | |
| contributor.author | Wunsch, Donald C. | |
| contributor.deptlab | Applied Computational Intelligence Laboratory | |
| contributor.deptlab | Electrical and Computer Engineering | |
| subject | Kalman filters | |
| subject | conjugate gradient methods | |
| subject | conjugate gradient training | |
| subject | daily closing price | |
| subject | feedforward neural nets | |
| subject | filtering theory | |
| subject | forecasting theory | |
| subject | learning (artificial intelligence) | |
| subject | low false alarm | |
| subject | multilayer perceptrons | |
| subject | multistream extended Kalman filter training | |
| subject | nonlinear filters | |
| subject | option trading | |
| subject | predictability analysis techniques | |
| subject | probabilistic neural networks | |
| subject | recurrent neural nets | |
| subject | recurrent neural networks | |
| subject | risk/reward ratio | |
| subject | short-term trends | |
| subject | stock markets | |
| subject | stock trend prediction | |
| subject | time delay neural networks | |
| subject | time series | |
| date.issued | 1998 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Saad, E.W.; Prokhorov, D.V.; Wunsch, D.C., II, "Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks," IEEE Transactions on Neural Networks, vol.9, no.6 pp.1456-1470, Nov 1998 | |
| identifier.issn | 1045-9227 | |
| identifier.pub.URI | ||
| description.abstract | Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We also discuss different predictability analysis techniques and perform an analysis of predictability based on a history of daily closing price. Our results indicate that all the networks are feasible, the primary preference being one of convenience | |
| type | Article - Journal | |
| 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 | 2007-04-05T14:04:31Z | |
| date.available | 2007-04-05T14:04:30Z | |
| identifier.persist.URI | ||
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