Stock trend prediction method coupled with multilevel indicators
To systematically incorporate multiple influencing factors,the coupled-state frequency memory(Co-SFM)network is proposed.This model integrates Copula estimation with neural networks,fusing multilevel data information,which is then fed into downstream learning modules.Co-SFM employs an upstream fusion module to incorporate multilevel data,thereby constructing a macro-plate-micro data structure.This configuration helps identify and integrate characteristics from different data levels,facilitating a deeper understanding of the internal links within the financial system.In the downstream model,Co-SFM uses a state-frequency memory network to mine hidden frequency information within stock prices,and the multifrequency patterns of sequential data are modeled.Empirical results show that Co-SFM's prediction accuracy for stock price trends is significantly better than that of other models.This is especially evident in multistep medium and long-term trend predictions,where integrating multilevel data results in notably improved accuracy.
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