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...
| المؤلفون الرئيسيون: | , , , |
|---|---|
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
Nature Portfolio
2024-01-01
|
| سلاسل: | Scientific Reports |
| الوصول للمادة أونلاين: | https://doi.org/10.1038/s41598-023-50783-0 |