Integrating Genetic Algorithms and Text Learning for Financial Prediction

James Thomas and Katia Sycara
Proceedings ofthe GECCO-2000 Workshop on Data Mining with Evolutionary Algorithms, July, 1999.


Download
  • Adobe portable document format (pdf) (106KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
This paper takes two approaches to prediction of financial markets using text data downloaded from web bulletin boards. The first uses maximum entropy text classification for prediction based on the whole body of text; the second uses a genetic algorithm to learn simple rules based solely on numerical data of trading volume, number of messages posted per day and total number of words posted per day. While both approaches produce positive excess returns in some cases, it is found that integrating the two predictors together produces far superior results. Furthermore, aggregating multiple GA trials to build single predictors increases performance even more.

Notes

Text Reference
James Thomas and Katia Sycara, "Integrating Genetic Algorithms and Text Learning for Financial Prediction," Proceedings ofthe GECCO-2000 Workshop on Data Mining with Evolutionary Algorithms, July, 1999.

BibTeX Reference
@inproceedings{Sycara_1999_3326,
   author = "James Thomas and Katia Sycara",
   title = "Integrating Genetic Algorithms and Text Learning for Financial Prediction",
   booktitle = "Proceedings ofthe GECCO-2000 Workshop on Data Mining with Evolutionary Algorithms",
   month = "July",
   year = "1999",
}