Computer Science

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).

PDF (PC)

1864

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
[1] GANDHMAL D P,KUMAR K.Systematic analysis and review of stock market prediction techni-ques.Computer Science Review,2019,34:100-190.
[2] CHAKRAVARTY S,DASH P K.A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices.Applied Soft Computing,2012,12(2):931-941.
[3] NI L,NI Z,GAO Y,et al.Stock trend prediction based on fractal feature selection and support vector machine.Expert Systems with Applications,2011,38(5):5569-5576.
[4] KRAUSS C,DO X A,HUCK N,et al.Deep neural networks,gradient-boosted trees,random forests:Statistical arbitrage on the S&P 500.European Journal of Operational Research,2017,259(2):689-702.
[5] FISCHER T,KRAUSS C.Deep learning with long short-term memory networks for financial market predictions.European Journal of Operational Research,2018,270(2):654-669
[6] HENRIQUE B M,SOBREIRO V A,KIMURA H,et al.Literature review:Machine learning techniques applied to financial market prediction.Expert Systems with Applications,2019,124(JUN):226-251.
[7] HOCHREITER S,SCHMIDHUBER J.Long short-term memory.Neural Computation,1997,9(8):1735-1780.
[8] GERS F A,SCHMIDHUBER,JRGEN,et al.Learning to Forget:Continual Prediction with LSTM.Neural Computation,2000,12(10):2451-2471.
[9] GRAVES A.Supervised Sequence Labelling with Recurrent Neural Networks.Springer,2012.
[10] QIN H M,SUN X.Classifying Bug Reports into Bugs and Nonbugs Using LSTM//The Tenth Asia-Pacifc Symposium on Internet-ware.2018.
[11] HUANG Y M,JIANG Y,HASAN T,et al.A Topic BiLSTM Model for Sentiment Classification//Innovation in Artificial Intelligence (ICIAI).2018.
[12] NELSON D M Q , PEREIRA A C M , OLIVEIRA R A D.Stock market’s price movement prediction with LSTM neural networks//International Joint Conference on Neural Networks (IJCNN).2017.
[13] LIN M,CHEN C X.Short-term prediction of stock market price based on GA optimization LSTM neurons//International Conference on Deep Learning Technologies (ICDLT).2018.
[14] SHIU J N,ZOU J Z,ZHANG J,et al.Research of stock price prediction based on dmd-lstm model .Application Research of Computers.https://doi.org/10.19734/J.issn.1001-3695.2018.08.0657.
[15] CHEN J,LIU D X,WU D S.Stock index forecasting method based on feature selection and LSTM model.Computer Engineering and Applications,2019,55(6):108-112.
[16] HO T K.Random decision forests//International Conference on Document Analysis and Recognition.1995:278-282.
[17] CHOLLET F.Keras.https://github.com/fchollet/keras,2016.
[18] Keras Documentation.https://keras.io.
[19] GRANGER C W J.Strategies for Modelling Nonlinear Time‐Series Relationships.Economic Record,2010,69(3):233-238.
[1] LENG Dian-dian, DU Peng, CHEN Jian-ting, XIANG Yang. Automated Container Terminal Oriented Travel Time Estimation of AGV [J]. Computer Science, 2022, 49(9): 208-214.
[2] NING Han-yang, MA Miao, YANG Bo, LIU Shi-chang. Research Progress and Analysis on Intelligent Cryptology [J]. Computer Science, 2022, 49(9): 288-296.
[3] HE Qiang, YIN Zhen-yu, HUANG Min, WANG Xing-wei, WANG Yuan-tian, CUI Shuo, ZHAO Yong. Survey of Influence Analysis of Evolutionary Network Based on Big Data [J]. Computer Science, 2022, 49(8): 1-11.
[4] LI Yao, LI Tao, LI Qi-fan, LIANG Jia-rui, Ibegbu Nnamdi JULIAN, CHEN Jun-jie, GUO Hao. Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network [J]. Computer Science, 2022, 49(8): 257-266.
[5] WANG Xin-tong, WANG Xuan, SUN Zhi-xin. Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network [J]. Computer Science, 2022, 49(8): 314-322.
[6] ZHANG Guang-hua, GAO Tian-jiao, CHEN Zhen-guo, YU Nai-wen. Study on Malware Classification Based on N-Gram Static Analysis Technology [J]. Computer Science, 2022, 49(8): 336-343.
[7] CHEN Ming-xin, ZHANG Jun-bo, LI Tian-rui. Survey on Attacks and Defenses in Federated Learning [J]. Computer Science, 2022, 49(7): 310-323.
[8] KANG Yan, XU Yu-long, KOU Yong-qi, XIE Si-yu, YANG Xue-kun, LI Hao. Drug-Drug Interaction Prediction Based on Transformer and LSTM [J]. Computer Science, 2022, 49(6A): 17-21.
[9] LI Ya-ru, ZHANG Yu-lai, WANG Jia-chen. Survey on Bayesian Optimization Methods for Hyper-parameter Tuning [J]. Computer Science, 2022, 49(6A): 86-92.
[10] ZHAO Lu, YUAN Li-ming, HAO Kun. Review of Multi-instance Learning Algorithms [J]. Computer Science, 2022, 49(6A): 93-99.
[11] YANG Hao-xiong, GAO Jing, SHAO En-lu. Vehicle Routing Problem with Time Window of Takeaway Food ConsideringOne-order-multi-product Order Delivery [J]. Computer Science, 2022, 49(6A): 191-198.
[12] WANG Fei, HUANG Tao, YANG Ye. Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion [J]. Computer Science, 2022, 49(6A): 784-789.
[13] WANG Shan, XU Chu-yi, SHI Chun-xiang, ZHANG Ying. Study on Cloud Classification Method of Satellite Cloud Images Based on CNN-LSTM [J]. Computer Science, 2022, 49(6A): 675-679.
[14] XIAO Zhi-hong, HAN Ye-tong, ZOU Yong-pan. Study on Activity Recognition Based on Multi-source Data and Logical Reasoning [J]. Computer Science, 2022, 49(6A): 397-406.
[15] YAO Ye, ZHU Yi-an, QIAN Liang, JIA Yao, ZHANG Li-xiang, LIU Rui-liang. Android Malware Detection Method Based on Heterogeneous Model Fusion [J]. Computer Science, 2022, 49(6A): 508-515.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.