The investment on the stock market is prone to be affected by the Internet. For the purpose of improving the prediction accuracy, we propose a multi-task stock prediction model that not only considers the stock correlations but also supports multi-source data fusion. Our proposed model first utilizes tensor to integrate the multisourced data, including financial Web news, investors' sentiments extracted from the social network and some quantitative data on stocks. In this way, the intrinsic relationships among different information sources can be captured, and meanwhile, multisourced information can be complemented to solve the data sparsity problem. Secondly, we propose an improved sub-mode coordinate algorithm (SMC). SMC is based on the stock similarity, aiming to reduce the variance of their subspace in each dimension produced by the tensor decomposition. The algorithm is able to improve the quality of the input features, and thus improves the prediction accuracy. And the paper utilizes the Long Short-Term Memory (LSTM) neural network model to predict the stock fluctuation trends. Finally, the experiments on 78 A-share stocks in CSI 100 and thirteen popular HK stocks in the year 2015 and 2016 are conducted. The results demonstrate the improvement on the prediction accuracy and the effectiveness of the proposed model.
Based on factors such as online news and investor sentiment to prediction, Jieyun Huang et al. REF designed tensors to capture the intrinsic connections of different sources of information and then used this method to solve the problem of data sparsity and finally proposed an improved submode coordinate algorithm (SMC) to match the use of tensors for improving the prediction accuracy.
A Tensor-Based Sub-Mode Coordinate Algorithm for Stock Prediction
2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)
2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)
Computer Science Mathematics