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...
Главные авторы: | Qi Li, Norshaliza Kamaruddin, Siti Sophiayati Yuhaniz, Hamdan Amer Ali Al-Jaifi |
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Формат: | Статья |
Язык: | English |
Опубликовано: |
Nature Portfolio
2024-01-01
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Серии: | Scientific Reports |
Online-ссылка: | https://doi.org/10.1038/s41598-023-50783-0 |
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