Finding attractive rules in stock markets using a modular genetic programming

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Finding attractive rules in stock markets using a modular genetic programming

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual conference on Genetic and evolutionary computation table of contents

Montreal, Québec, Canada

POSTER SESSION: Track 13: real world application table of contents

Pages 1933-1934

Year of Publication: 2009

ISBN:978-1-60558-325-9

Authors

Seung-Kyu Lee	Seoul National University, Seoul, South Korea
Byung-Ro Moon	Seoul National University, Seoul, South Korea

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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ABSTRACT

We propose a new modular genetic programming for finding attractive and statistically sound technical rules. We restrict the problem space using well-known technical rules to discover attractive technical rules. Experimental results show that our modular genetic programming can successfully find unknown attractive technical rules for Korean stock market.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

S. Nisson. Japanese candlestick charting techniques: A contemporary guide to the ancient investment techniques of the Far East. New York Institute of Finance, 2001.

INDEX TERMS

Primary Classification:
J. Computer Applications
J.m MISCELLANEOUS