A local and global event sentiment based efficient stock exchange forecasting using deep learning
作者：
Highlights：
• A deep learning-based method is proposed to forecast stock prices for top companies from four countries that were selected from developed, underdeveloped and emerging economies.
• We investigate the effect of different famous events occurred from 2012 to 2016 comprising 11.42 Million tweets on stock prediction.
• The effect of local and global events for stock companies from each country has been investigated.
• The results show that event sentiment improves the results for stock forecasting depending upon local and global events.
摘要
•A deep learning-based method is proposed to forecast stock prices for top companies from four countries that were selected from developed, underdeveloped and emerging economies.•We investigate the effect of different famous events occurred from 2012 to 2016 comprising 11.42 Million tweets on stock prediction.•The effect of local and global events for stock companies from each country has been investigated.•The results show that event sentiment improves the results for stock forecasting depending upon local and global events.
论文关键词：Stock prediction,Regression,Deep learning,Event sentiment
论文评审过程：Received 10 December 2018, Revised 18 July 2019, Accepted 18 July 2019, Available online 31 July 2019, Version of Record 21 November 2019.
论文官网地址：https://doi.org/10.1016/j.ijinfomgt.2019.07.011
