Good news: Using news feeds with genetic programming to predict stock prices

Fiacc Larkin, Conor Ryan

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

Abstract

This paper introduces a new data set for use in the financial prediction domain, that of quantified News Sentiment. This data is automatically generated in real time from the Dow Jones network with news stories being classified as either Positive, Negative or Neutral in relation to a particular market or sector of interest. We show that with careful consideration to fitness function and data representation, GP can be used effectively to find non-linear solutions for predicting large intraday price jumps on the S&P 500 up to an hour before they occur. The results show that GP was successfully able to predict stock price movement using these news alone, that is, without access to even current market price.

Original languageEnglish
Title of host publicationGenetic Programming - 11th European Conference, EuroGP 2008, Proceedings
Pages49-60
Number of pages12
DOIs
Publication statusPublished - 2008
Event11th European Conference on Genetic Programming, EuroGP 2008 - Naples, Italy
Duration: 26 Mar 200828 Mar 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4971 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th European Conference on Genetic Programming, EuroGP 2008
Country/TerritoryItaly
CityNaples
Period26/03/0828/03/08

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