Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming
Qi Li,
Norshaliza Kamaruddin,
Siti Sophiayati Yuhaniz
et al.
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 over the period from 2014 to 2022. Using the S&P Alpha Pool Dataset for China as basic input, this architecture incorporates data augmentation and feature extraction techniques. The result of this study demonstrates signif… Show more
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