TY - GEN
T1 - A new convolutional neural network and long short term memory combined model for stock index prediction
AU - Lin, Yuyang
AU - Zhong, Qiyin
AU - Huang, Qi
AU - Li, Muyang
AU - Ma, Fei
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Stock market is one of the most important parts in the financial market. Numerous time series forecasting methods have been developed for predicting the stock price. Feature extraction is essential to many of these forecasting models. Highly related features can improve the accuracy of the forecasting model. This paper proposes a new model named CNN-LS that combines Convolution Neural Networks (CNN) with Long Short-Term Memory (LSTM) to predict the price of six common indices, including Shanghai Composite Index, Shenzhen Component Index, Dow Jones Index, Nasdaq Index, Nikkei 225 and SP 500. The model contains two paths of CNN and one path of LSTM to extract features. In our experiment with 10 years historic data of six indexes, the proposed CNN-LS achieved MSE of 0.5994 and MAE of 0.5427 on the testing set, both of which are better than MAE and MSE of five recent methods for stock prediction.
AB - Stock market is one of the most important parts in the financial market. Numerous time series forecasting methods have been developed for predicting the stock price. Feature extraction is essential to many of these forecasting models. Highly related features can improve the accuracy of the forecasting model. This paper proposes a new model named CNN-LS that combines Convolution Neural Networks (CNN) with Long Short-Term Memory (LSTM) to predict the price of six common indices, including Shanghai Composite Index, Shenzhen Component Index, Dow Jones Index, Nasdaq Index, Nikkei 225 and SP 500. The model contains two paths of CNN and one path of LSTM to extract features. In our experiment with 10 years historic data of six indexes, the proposed CNN-LS achieved MSE of 0.5994 and MAE of 0.5427 on the testing set, both of which are better than MAE and MSE of five recent methods for stock prediction.
KW - Close Price Prediction
KW - Convolutional neural network
KW - Deep learning
KW - Long Short-Term Memory
KW - Stock index
UR - http://www.scopus.com/inward/record.url?scp=85123463250&partnerID=8YFLogxK
U2 - 10.1109/CISP-BMEI53629.2021.9624337
DO - 10.1109/CISP-BMEI53629.2021.9624337
M3 - Conference Proceeding
AN - SCOPUS:85123463250
T3 - Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
BT - Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
A2 - Li, Qingli
A2 - Wang, Lipo
A2 - Wang, Yan
A2 - Li, Wenwu
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
Y2 - 23 October 2021 through 25 October 2021
ER -