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...
Автори: | Qi Li, Norshaliza Kamaruddin, Siti Sophiayati Yuhaniz, Hamdan Amer Ali Al-Jaifi |
---|---|
Формат: | Стаття |
Мова: | English |
Опубліковано: |
Nature Portfolio
2024-01-01
|
Серія: | Scientific Reports |
Онлайн доступ: | https://doi.org/10.1038/s41598-023-50783-0 |
Схожі ресурси
Схожі ресурси
-
Forecasting stock prices with long-short term memory neural network based on attention mechanism.
за авторством: Jiayu Qiu, та інші
Опубліковано: (2020-01-01) -
A HYBRID RECURRENT NEURAL NETWORK AND LONG SHORT-TERM MEMORY FOR SIMPLIFIED GENERAL PERTURBATIONS-4 MODEL IN ORBIT PROPAGATION /
за авторством: Nor'Asnilawati Salleh, 1982-, author 362370, та інші
Опубліковано: (2022) -
A HYBRID RECURRENT NEURAL NETWORK AND LONG SHORT-TERM MEMORY FOR SIMPLIFIED GENERAL PERTURBATIONS-4 MODEL IN ORBIT PROPAGATION /
за авторством: Nor'Asnilawati Salleh, 1982-, author 362370, та інші
Опубліковано: (2022) -
Long run dynamic relationships between oil prices, exchange rates, stock market and interest rate in Malaysia
за авторством: Nordin, Sabariah, та інші
Опубліковано: (2018) -
The comparison stateless and stateful LSTM architectures for short-term stock price forecasting
за авторством: Anna Chadidjah, та інші
Опубліковано: (2024-01-01)