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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Qi Li, Norshaliza Kamaruddin, Siti Sophiayati Yuhaniz, Hamdan Amer Ali Al-Jaifi
Μορφή: Άρθρο
Γλώσσα:English
Έκδοση: Nature Portfolio 2024-01-01
Σειρά:Scientific Reports
Διαθέσιμο Online:https://doi.org/10.1038/s41598-023-50783-0