A CNN-LSTM-Based Model to Forecast Stock Prices

Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM...

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Main Authors: Wenjie Lu, Jiazheng Li, Yifan Li, Aijun Sun, Jingyang Wang
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/6622927
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author Wenjie Lu
Jiazheng Li
Yifan Li
Aijun Sun
Jingyang Wang
author_facet Wenjie Lu
Jiazheng Li
Yifan Li
Aijun Sun
Jingyang Wang
author_sort Wenjie Lu
collection DOAJ
description Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data.
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spelling doaj-art-7aee1b40204a487890cd0f6d38fa9b592025-02-03T01:00:40ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/66229276622927A CNN-LSTM-Based Model to Forecast Stock PricesWenjie Lu0Jiazheng Li1Yifan Li2Aijun Sun3Jingyang Wang4Business School, Jiangsu Second Normal University, Nanjing 210000, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaBusiness School, Jiangsu Second Normal University, Nanjing 210000, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaStock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data.http://dx.doi.org/10.1155/2020/6622927
spellingShingle Wenjie Lu
Jiazheng Li
Yifan Li
Aijun Sun
Jingyang Wang
A CNN-LSTM-Based Model to Forecast Stock Prices
Complexity
title A CNN-LSTM-Based Model to Forecast Stock Prices
title_full A CNN-LSTM-Based Model to Forecast Stock Prices
title_fullStr A CNN-LSTM-Based Model to Forecast Stock Prices
title_full_unstemmed A CNN-LSTM-Based Model to Forecast Stock Prices
title_short A CNN-LSTM-Based Model to Forecast Stock Prices
title_sort cnn lstm based model to forecast stock prices
url http://dx.doi.org/10.1155/2020/6622927
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