Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong
作者：
Highlights：
• Based on both technical indicators and news sentiments, the LSTM models outperform the MKL and the SVM in both prediction accuracy and F1 score.
• The LSTM models incorporating both information sources outperform the models that only use either technical indicators or news sentiments, in both individual stock level and sector level.
• Among the four sentiment dictionaries, finance domain specific sentiment dictionary (Loughran-McDonald Financial Dictionary) models the new sentiments better, which brings at most 120% prediction performance improvement, comparing with the other three dictionaries (at most 50%).
摘要
•Based on both technical indicators and news sentiments, the LSTM models outperform the MKL and the SVM in both prediction accuracy and F1 score.•The LSTM models incorporating both information sources outperform the models that only use either technical indicators or news sentiments, in both individual stock level and sector level.•Among the four sentiment dictionaries, finance domain specific sentiment dictionary (Loughran-McDonald Financial Dictionary) models the new sentiments better, which brings at most 120% prediction performance improvement, comparing with the other three dictionaries (at most 50%).
论文关键词：Stock prediction,News sentiment analysis,Deep learning,00-01,99-00
论文评审过程：Received 30 July 2019, Revised 20 January 2020, Accepted 21 January 2020, Available online 12 February 2020, Version of Record 19 June 2020.
论文官网地址：https://doi.org/10.1016/j.ipm.2020.102212
