Abstract
The stock market is affected by economic market, policy, and other factors, and its internal change law is extremely complex. With the rapid development of the stock market and the expansion of the scale of investors, the stock market has produced a large number of transaction data, which makes it more difficult to obtain valuable information. Because deep neural network is good at dealing with the prediction problems with large amount of data and complex nonlinear mapping relationship, this paper proposes an attention-guided deep neural network stock prediction algorithm. This paper synthesizes the daily stock social media text emotion index and stock technology index as the data source and applies them to the long-term and short-term memory neural network (LSTM) model to predict the stock market. The stock emotion index is extracted by constructing a social text classification emotion model of bidirectional long-term and short-term memory neural network (Bi-LSTM) based on attention mechanism and glove word vector representation algorithm. In addition, a dimensionality reduction model based on decision tree (DT) and principal component analysis (PCA) is constructed to reduce the dimensionality of stock technical indicators and extract the main data information. Furthermore, this paper proposes a model based on nasNet for pattern recognition. The recognition results can be used to automatically identify short-term K-line patterns, predict reliable trading signals, and help investors customize short-term high-efficiency investment strategies. The experimental results show that the prediction accuracy of the proposed algorithm can reach 98.6%, which has high application value.
Highlights
The stock market is an important part of a country’s economy, which seriously affects the formulation of individual and national investment strategies
The accuracy of the convolutional neural network (CNN)+Bi-long-term and short-term memory neural network (LSTM) model is 7% lower than that of the constructed classification, mainly because the social media text is relatively short and colloquial, while CNN structure is good at dealing with long text structure
In order to verify the effect of the improved nesNet model on the prediction of stock closing price with time series characteristics, LSTM model, ridge regression, kneighbors, decision tree, and support vector machine (SVM) algorithm are introduced as the comparison group
Summary
The stock market is an important part of a country’s economy, which seriously affects the formulation of individual and national investment strategies. The data-driven stock market forecast provides a more reliable buying and selling signal for the automatic trading strategy, which can maximize the user’s investment income. In the field of data analysis, compared with earlier studies, researchers realize that the stock market is a whole composed of a large number of stocks, and there is a high correlation between stock indexes; at the same time, the latest development of sensor networks and communication technology makes it possible to collect massive stock data, so how to effectively process massive stock data, successfully predict the change trend of stock market, and capture the behavior mode of stock market has become the focus of research [4]. Literature [6] believes that the nonlinearity and nonstationary of stock data lead to the limited application of traditional multiple regression and linear regression models. The deep neural network method based on attention is used to predict the stock in the paper
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