Stock Price Prediction Based on Natural Language Processing1

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Abstract
The keywords used in traditional stock price prediction are mainly based on literature and experience. This study designs a new text mining method for keywords augmentation based on natural language processing models including Bidirectional Encoder Representation from Transformers (BERT) and Neural Contextualized Representation for Chinese Language Understanding (NEZHA) natural language processing models. The BERT vectorization and the NEZHA keyword discrimination models extend the seed keywords from two dimensions of similarity and importance, respectively, thus constructing the keyword thesaurus for stock price prediction. Furthermore, the predictive ability of seed words and our generated words are compared by the LSTM model, taking the CSI 300 as an example. The result shows that, compared with seed keywords, the search indexes of extracted words have higher correlations with CSI 300 and can improve its forecasting performance. Therefore, the keywords augmentation model designed in this study is helpful to provide references for other variable expansion in financial time series forecasting.
Funding Information
  • National Natural Science Foundation of China (21&ZD149)

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