Learning to trade with insider information

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Learning to trade with insider information

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ACM International Conference Proceeding Series; Vol. 258 archive
Proceedings of the ninth international conference on Electronic commerce table of contents

Minneapolis, MN, USA

SESSION: Session M8: electronic commerce table of contents

Pages: 169 - 176

Year of Publication: 2007

ISBN:978-1-59593-700-1

Author

Sanmay Das

University of California: San Diego, La Jolla, CA

Sponsors

SIGART: ACM Special Interest Group on Artificial Intelligence
ACM: Association for Computing Machinery
SIGEcom: ACM Special Interest Group on Electronic Commerce

Publisher

ACM New York, NY, USA

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ABSTRACT

This paper introduces algorithms for learning how to trade using insider (superior) information in Kyle's model of financial markets. Prior results in finance theory relied on the insider having perfect knowledge of the structure and parameters of the market. I show here that it is possible to learn the equilibrium trading strategy when its form is known even without knowledge of the parameters governing trading in the model. However, the rate of convergence to equilibrium is slow, and an approximate algorithm that does not converge to the equilibrium strategy achieves better utility when the horizon is limited. I analyze this approximate algorithm from the perspective of reinforcement learning and discuss the importance of domain knowledge in designing a successful learning algorithm.

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.

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