Abstract: How to make an accurate financial time series prediction is one of the important quantitative financial problems.Long and short term memory neural network (LSTM) has solved the complex serialized data learning problems such as stock prediction much better.However,the results of previous studies show that there are still some problems such as unbalanced prediction and local minimum value,which lead to poor prediction ability.Based on the above problems,the genetic algorithm (GA) is used to solve the parameter adJustment problem to ensure the balance of model prediction,and a new stock prediction model is constructed.First,LSTM neural network is used to predict closing price.Then,the prediction results are calculated to the Judgment method based on genetic algorithm.Finally,the predicted stock’s ups and downs signals are gained as the output.This model is different from the previous state-of-the-art and is mainly improved for the output module of the LSTM model.High-frequency trading data of Index China are used for verification.The results show that the improved model is better than the LSTM model.

Key words: Long short-term memory, Genetic Algorithm, Machine learning, Stock prediction