Research and application of mapping relationship based on learning attention mechanism

Abstract

The study on the interactions between different or the same variables of financial markets is an interesting topic. Many efforts have been devoted to investigate this issue. However, there has been little work studying the relationship of the various attributes within the stock, while this relationship is essential for us to have a deeper understanding of stock’s internal mechanisms. So in this paper, we explored using sequence-to-sequence model for extracting the relationship of arbitrarily two properties of the stock. We not only give a qualitative description of the relationship between stock’s attributes, but also quantify the relationship through the model. The experimental results show that there are certain correlations between the internal attributes of the stock, among which the correlation between Close& % Tuv and % Chg& % Tuv are more prominent. In addition, we also conducted the anomaly detection on network public opinion information, and found out the starting points of abnormal events combined with the network news information. By comparing the starting points of the events and the changes in the relationship between stock attributes, we concluded that there is a certain regularity between them.