Quantifying StockTwits semantic terms’ trading behavior in financial markets: An effective application of decision tree algorithms

作者：

Highlights：

• Provide linkage between changes in volume semantic terms and subsequent market moves.

• Sell short at higher prices resulted from decreased appearance of negative words.

• Buy or take long positions resulted from increased appearance of positive words.

• StockTwits contains valuable information and precede trading activity in the market.

摘要

•Provide linkage between changes in volume semantic terms and subsequent market moves.•Sell short at higher prices resulted from decreased appearance of negative words.•Buy or take long positions resulted from increased appearance of positive words.•StockTwits contains valuable information and precede trading activity in the market.

论文关键词：Decision tree,Filter approach,Text mining,Trading Strategy

论文评审过程：Received 30 April 2015, Revised 31 July 2015, Accepted 4 August 2015, Available online 10 August 2015, Version of Record 4 September 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.08.008