A Novel Stock Index Intelligent Prediction Algorithm Based on Attention‐Guided Deep Neural Network

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.

Implications of Deep Learning for Stock Market Forecasting

“…Research has also explored innovative approaches such as the use of kernel adaptive filtering in a stock market dependency framework for predicting stock returns, long short-term memory networks optimized with genetic algorithms, and attention-guided deep neural networks for stock index prediction. These methods aim to provide reliable trading signals and assist investors in adapting efficient investment strategies [25] [26]. Additionally, research has explored combining multi-source data, graphical neural networks, and extended hidden Markov models to improve the accuracy of stock market predictions.…”