基于优化LSTM模型的股票预测

doi:10.11896/jsjkx.200400011

摘要/Abstract

摘要： 股票预测研究一直是困扰投资者的难题。以往,投资者采用传统分析方法如K线图、十字线等方法来预测股票走势,但随着科技的进步和经济市场的发展,以及经济政策的变动,股票的价格走势受到越来越多方面因素的干扰,仅靠传统的分析方法远远不能解析出股票价格波动中隐藏着的重要信息,因此预测精度大打折扣。为了提高股票价格的预测精度,提出一种基于PCA和LASSO的LSTM神经网络股票价格预测模型。采用2015－2019年平安银行(000001)五大类技术指标数据,通过PCA和LASSO方法对五大类技术分析指标进行降维筛选,再使用LSTM模型进行平安银行股票收盘价预测,对比前两种模型和单纯使用LSTM模型的预测效果稳定性及准确性。结果表明,相比于LASSO-LSTM模型和LSTM模型,PCA-LSTM模型能够大幅削减数据冗余,并且获得了更优异的预测精度。

关键词: LASSO, LSTM, PCA, 技术指标, 平安银行

Abstract: Stock forecasting research has always been a problem that plagued investors.In the past,investors used traditional analysis methods such as K-line diagrams and Yin-Yang lines to predict stock trends.However,with the advancement of science and technology,the development of economic markets,and changes in economic policies,the price trend of a stock is disturbed by various factors.Traditional analysis methods are far from being able to analyze theinformation in volatility of a stock.So prediction accuracy is greatly reduced.In order to improve the prediction accuracy of stock prices,this paper proposes a stock price prediction model based on PCA,LASSO,and LSTM neural networks.Based on the data of the five major categories of technical indicators of Ping An Bank (000001) from 2015 to 2019,the five major categories of indicators are reduced and screened using the PCA and LASSO methods,and the LSTM model is used to predict the closing price of Ping An Bank's stock,compared with the stability and accuracy ofthe previous two methods and using LSTM alone.The experimental results show that the PCA-LSTM model significantly reduces data redundancy and obtains better prediction accuracy than the LASSO-LSTM model and LSTM model.

Key words: LASSO, LSTM, PCA, Ping An bank, Technical index

中图分类号:

TP183

胡聿文. 基于优化LSTM模型的股票预测[J]. 计算机科学, 2021, 48(6A): 151-157. https://doi.org/10.11896/jsjkx.200400011

HU Yu-wen. Stock Forecast Based on Optimized LSTM Model[J]. Computer Science, 2021, 48(6A): 151-157. https://doi.org/10.11896/jsjkx.200400011

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