Entropy based measure sentiment analysis in the financial market

Qiang Song, Saud Almahdi, Steve Y. Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Financial markets are usually affected by the news media which would allow us to consider the news sentiment effect on the market. In this study we propose the sentiment shock and sentiment trend. We provide an entropy based analysis for the two and compare them with the original sentiment score where we quantify the effectiveness of each sentiment in different financial sectors. We show that the sentiment shock and the sentiment trend are more efficient for cyclical sectors and both have a better capability to reduce the noise in the original signal around it's mean. Also, we design a dynamic sentiment shock and trend trading strategy with weekly evaluations where we show that a strategy based on sentiment shock or trend can have a superior performance compared to a buy-and-hold strategy in terms of risk-adjusted return.

Original languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Pages1-5
Number of pages5
ISBN (Electronic)9781538627259
DOIs
StatePublished - 1 Jul 2017
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: 27 Nov 20171 Dec 2017

Publication series

Name2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Volume2018-January

Conference

Conference2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Country/TerritoryUnited States
CityHonolulu
Period27/11/171/12/17

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