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...

Täydet tiedot

Bibliografiset tiedot
Päätekijät: Qi Li, Norshaliza Kamaruddin, Siti Sophiayati Yuhaniz, Hamdan Amer Ali Al-Jaifi
Aineistotyyppi: Artikkeli
Kieli:English
Julkaistu: Nature Portfolio 2024-01-01
Sarja:Scientific Reports
Linkit:https://doi.org/10.1038/s41598-023-50783-0