Prediction of Stock Market Trend Based on Multi-Agent Transmission Relationship
Stock market trend prediction is a challenging problem in the field of machine learning.Owing to the dynamic and uncertain nature of some factors affecting the stock market,changes in stock market trends are difficult to predict.In response to the issues of poor sensitivity and weak adaptability in stock market prediction,starting from the transmission relationship between fast and slow variables,a Multi-Agent Transmission Influence Diagram(MATID)stock market trend prediction method is proposed using Agent technology to jointly model the fast and slow cycles in the stock market.First,the classification criteria of fast and slow periods in the stock market are provided,and the concept of period energy is introduced.Subsequently,a quantitative calculation method for periodic energy through feature fusion of relevant trend indicators is provided;the transmission factor representation method is provided by analyzing the dynamic action process of fast and slow periods.Thereafter,fast and slow periods are corresponded to different Agent,and a multi-Agent influence diagram model is used to model the transmission process of the fast and slow cycles.Furthermore,the stock market oscillator model is used to represent the transmission utility between fast and slow Agent.Finally,the automatic inference technique of a joint tree is used to predict the stock market trend.Experimental results under different sample sizes and different stock market trends demonstrate that compared with Gated Recurrent Unit(GRU),Stacked Long Short-Term Memory(S-LSTM),and Hybrid Recurrent Neural Network(Hybrid-RNN)prediction methods,the MATID method has good sensitivity and adaptability with approximately 1.5%‒7.0%,5.4%‒6.7%,and 3.7%‒6.2%improvements in prediction,recall,and F1 value,respectively.
Multi-Agent Transmission Influence Diagram(MATID)periodic transmissionoscillator modelutility functionjoint tree
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