ThomasSycara00

2013-02-21 (木) 18:38:47 (2842d) | Topic path: Top / ThomasSycara00

テキスト分析

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.8655 http://www.technicalanalysis.org.uk/moving-averages/ThSy00.pdf

Integrating genetic algorithms and text learning for financial prediction (2000) by James D Thomas, Katia Sycara in Proceedings of the Genetic and Evolutionary Computing 2000 Conference Workshop on Data Mining with Evolutionary Algorithms, Las Vegas

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 to predict 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.

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