Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 467-473.doi: 10.11896/JsJkx.190900128

• Database & Big Data & Data Science • Previous Articles     Next Articles

Model for Stock Price Trend Prediction Based on LSTM and GA

BAO Zhen-shan1, GUO Jun-nan1, XIE Yuan2 and ZHANG Wen-bo1   

  1. 1 Faulty of Information Technology,BeiJing University of Technology,BeiJing 100124,China
    2 Commando Capital Company,BeiJing 100600,China
  • Published:2020-07-07
  • About author:BAO Zhen-shan, born in 1965, is a mem-ber of China Computer Federation.His main research interests include machine learning and Financial technology.
    ZHANG Wen-bo, born in 1980, Ph.D, lecturer, is a member of China Compu-ter Federation.Her main research inte-rests include heterogeneous computing and trust computing.
  • Supported by:
    This work was supported by the National Key R&D Program of China (2017YFC0803300).

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

CLC Number: 

  • TP183
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