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Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks
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.
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Cites
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Numerical recipes in C: the art of scientific computing
(1993) · Choice Reviews Online · doi:10.5860/choice.30-5638 · Cited by 17,933
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,031
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459
Methods of conjugate gradients for solving linear systems
Hestenes, M. R., & Stiefel, E. (1952) · Journal of Research of the National Bureau of Standards · doi:10.6028/jres.049.044 · Cited by 8,132
Function minimization by conjugate gradients
Fletcher, R. (1964) · The Computer Journal · doi:10.1093/comjnl/7.2.149 · Cited by 4,939
Backpropagation through time: what it does and how to do it
Werbos, P. J. (1990) · Proceedings of the IEEE · doi:10.1109/5.58337 · Cited by 4,879
Characterization of Strange Attractors
Grassberger, P., & Procaccia, I. (1983) · Physical Review Letters · doi:10.1103/physrevlett.50.346 · Cited by 4,857
Independent coordinates for strange attractors from mutual information
Fraser, A. M., & Swinney, H. L. (1986) · Physical Review A · doi:10.1103/physreva.33.1134 · Cited by 4,606
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Metadata
- Authors
- Saad, E. W., Prokhorov, D. V., & Wunsch, D. C.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1998
- Volume
- 9
- Issue
- 6
- Pages
- 1456-1470
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2158663270
- Cited by
- 537
- References
- 52
- FWCI
- 12.55
- Type
- journal article
- Language
- en
- Open access
- closed
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