Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming
Abstract This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market ov...
| Huvudupphovsmän: | , , , |
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| Materialtyp: | Artikel |
| Språk: | English |
| Publicerad: |
Nature Portfolio
2024-01-01
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| Serie: | Scientific Reports |
| Länkar: | https://doi.org/10.1038/s41598-023-50783-0 |