Deep Learning-based Integrated Framework for stock price movement prediction - Zhao, Yanli; Yang, Guang (Applied soft computing, 2023)
Stock market prediction is a very important problem in the economics field. With the development of machine learning, more and more algorithms are applied in the stock market to predict the stock price movement. However, stock market prediction is regarded as a challenging task for the noise and volatility of stock market data. Therefore, in this paper, a novel hybrid model SA-DLSTM is proposed to predict stock market and simulation trading by combine a emotion enhanced convolutional neural network (ECNN), the denoising autoencoder (DAE) models, and long short-term memory model (LSTM). Firstly, user-generated comments on Internet were used as a complement to stock market data, and ECNN was applied to extract the sentiment representation. Secondly, we extract the key features of stock market data by DAE, which can improve the prediction accuracy. Thirdly, we take the timeliness of emotion on stock market into consideration and construct more reliable and realistic sentiment indexes. Finally, the key features of stock data and sentiment indexes are fed into LSTM to make stock market prediction. Experiment results show that the prediction accuracy of SA-DLSTM are superior to other compared models. Meanwhile, SA-DLSTM has a good performance both in return and risk. It can help investors make wise decisions. •The combination of public opinions and sentiments, SA-DLSTM can provide robust and accurate predictions for the stock market trends.•SA-DLSTM can calculate the sentiment indexes in which they are with different emotion strength, and use an exponential time function to calculate the timeliness of emotion.•SA-DLSTM can extract different features from multivariate financial time series, and integrate features for further classification.