Observing the Performance of Long-Short Term Memory with Symbolic Genetic Algorithm in Predicting Stock Price Change on Fundamental Indicators

Inspired with the research, we present an enhanced Deep Neural Network (DNN) framework that combines Symbolic Genetic Algorithm (SGA) with Long-Short Term Memory (LSTM) in predicting the cross-sectional price returns using 245 fundamental indicators in China. The study addresses the challenges posed by fundamental indicators resembling smart beta factors in efficient markets . The proposed DNN framework incorporates data augmentation and feature selection techniques, resulting in significant improvements in Rank Information coefficient (Rank IC) and IC information ratio (ICIR) of 1,128% and 5,360% . Additionally, a rule-based strategy outperforms major Chinese stock indexes, generating impressive an average ("alpha") 9.89%