|Table of Contents|

Deep Learning Stock Price Forecasting Method Based on Plate Effect(PDF)

南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年01期
Page:
30-38
Research Field:
机器学习
Publishing date:

Info

Title:
Deep Learning Stock Price Forecasting Method Based on Plate Effect
Author(s):
Li QingtaoLin PeiguangWang JihouZhou JiaqianZhang YanJian Muwei
School of Computer Science and Technology,Shandong University of Finance and Economics,Jinan 250014,China
Keywords:
characteristics of the same industry stocksXGBooststock predictionLSTMdeep learning
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2022.01.005
Abstract:
As an important research direction in the field of financial forecasting,accurate prediction of stock price rise and fall can help investors to make profits or stop losses in time. It has been found that certain factors(such as policies,social emergencies)can have an impact on the prices of multiple stocks in the same sector,resulting in similar movements of multiple stocks in the same sector in a certain period of time,i.e. the sector effect. Therefore,the price trends of multiple stocks under the same segment are useful for stock forecasting. To address this phenomenon,a deep learning stock price trend prediction method based on the plate effect is proposed. Firstly,the Pearson correlation coefficient and XGBoost algorithm are used to analyze the closing prices of many stocks in the same sector so as to screen out the stocks with high correlation with the predicted stocks. Then,the autoencoder is used to reduce the dimension of the closing prices of these stocks,so as to extract the price trend of the stocks. Secondly,a hybrid deep learning prediction model based on convolutional neural network and long short-term memory network is constructed. One-dimensional convolutional neural network is used to extract the features of input data,and LSTM network is used to predict stock prices. The model uses stocks in four sectors,namely,banking,pharmaceuticals,alcohol,and entertainment media,as the experimental data set. In order to improve the prediction effect of the model,the number of neurons of the LSTM network is simply analyzed by random search to select the better number of neurons. Finally,the experimental analysis shows that the deep learning prediction model based on the same board dataset has good prediction effect.

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Last Update: 2022-03-15