Prediction of Financial Big Data Stock Trends Based on Attention Mechanism

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%.

[1]  Alexandros Iosifidis,et al.  Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks , 2017, 2017 IEEE 19th Conference on Business Informatics (CBI).

[2]  Lili Zhang,et al.  The Prediction Research of Safety Stock Based on the Combinatorial Forecasting Model , 2015, ICCSE 2015.

[3]  Bingsheng He,et al.  Efficient Gradient Boosted Decision Tree Training on GPUs , 2018, 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

[4]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[5]  Chen Ping,et al.  Forecast of Yearly Stock Returns Based on Adaboost Integration Algorithm , 2017, 2017 IEEE International Conference on Smart Cloud (SmartCloud).

[6]  Adriano C. M. Pereira,et al.  Stock market's price movement prediction with LSTM neural networks , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[7]  Chihming Hsu,et al.  Forecasting the Stock Price Volatilities by Integratingthe Support Vector Regression and the Krill Herd Algorithm , 2017, 2017 International Conference on Computing Intelligence and Information System (CIIS).