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Carnegie Mellon Robotics Institute

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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",
}

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