Transformer-Based Attention Network For Stock Movement Prediction

Transformer-Based Attention Network For Stock Movement Prediction. Web the fusion of the transformer model and various attention mechanisms is introduced for the first time for stock movement prediction to construct teanet, in which the transformer model is used to extract deep features of small samples and multiple attention mechanisms are used to capture dependencies and obtain key information. Web stock movement prediction is to predict the future movements of stocks for investment, which is challenging both for research and industry.

Web stock movement prediction is to predict the future movements of stocks for investment, which is challenging both for research and industry. Web according to the efficient market hypothesis (emh) [fama et al., 1969], stock price movements are thought to be related to the news. Typically, stock movement is predicted based on financial news.