Stock trend prediction method coupled with multilevel indicators

Abstract

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.