Using extended classifier system to forecast S&P futures based on contrary sentiment indicators

An-Pin Chen*, Yung Hua Chang

*Corresponding author for this work

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

8 Scopus citations

Abstract

This research demonstrates the accurate forecasting performance of extended classifier system (XCS) based on contrary sentiment indicators in predicting S&P 500 futures. These indicators include volatility index, put-call ratio, and trading index. To prove that XCS based on sentiment indicators can fit the financial forecasting domain, the performance of XCS is compared with that of three trading strategies, including buy-and-hold, trend-following, and mean-reversion strategies over the same sample period. The simulation results showed that XCS based on contrary sentiment indicators possesses both forecasting accuracy and profits earning capability in the real world.

Original languageEnglish
Title of host publication2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Pages2084-2090
Number of pages7
DOIs
StatePublished - 31 Oct 2005
Event2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom
Duration: 2 Sep 20055 Sep 2005

Publication series

Name2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Volume3

Conference

Conference2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
CountryUnited Kingdom
CityEdinburgh, Scotland
Period2/09/055/09/05

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