Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming

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...

Podrobná bibliografie
Hlavní autoři:	Qi Li, Norshaliza Kamaruddin, Siti Sophiayati Yuhaniz, Hamdan Amer Ali Al-Jaifi
Médium:	Článek
Jazyk:	English
Vydáno:	Nature Portfolio 2024-01-01
Edice:	Scientific Reports
On-line přístup:	https://doi.org/10.1038/s41598-023-50783-0

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