Prediction of Financial Big Data Stock Trends Based on Attention Mechanism

Abstract

Stock trend prediction has always been the focus of research in the field of financial big data. Stock data is complex nonlinear data, while stock price is changing over time. Based on the characteristics of stock data, this paper proposes a financial big data stock trend prediction algorithm based on attention mechanism (STPA). We adopt Bidirectional Gated Recurrent Unit (BGRU) and attention mechanism to capture the long-term dependence of data on time series. The attention mechanism is used to analyze the weight of the impact of data from different time periods on the trend prediction results, thereby reducing the error of stock data change trend prediction and improving the accuracy of trend prediction. We select the daily closing price data of 10 stocks for model training and performance evaluation. Experimental results demonstrate that the proposed method STPA achieves higher precision, recall rate and F1-Score in predicting stock change trends than the other methods. Compared with mainstream methods, STPA improves the precision by 4%, improves recall by 2.5%, and improves F1-Score by 3.2%.