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Inspecting the Latent Space of Stock Market Data with Genetic Programming

Authors:

Sungjoo Ha,

Sangyeop Lee,

Byung-Ro MoonAuthors Info & Claims

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

Pages 63 - 64

https://doi.org/10.1145/2908961.2909004

Published: 20 July 2016 Publication History

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Abstract

We suggest a method of inspecting the latent space of stock market data using genetic programming. Given black box patterns and (stock, day) tuples a relation matrix is constructed. Applying a low-rank matrix factorization technique to the relation matrix induces a latent vector space. By manipulating the latent vector representations of black box patterns, the geometry of the latent space can be examined. Genetic programming constructs a tree representation corresponding to an arbitrary latent vector representation, allowing us to interpret the result of the inspection.

References

[1]

S. Ha and B. R. Moon. Fast Knowledge Discovery in Time Series with GPGPU on Genetic Programming. Genetic and Evolutionary Computation Conference, pages 1159--1166, 2015.

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[2]

D. D. Lee and H. S. Seung. Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems 13, pages 556--562. 2001.

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Index Terms

Inspecting the Latent Space of Stock Market Data with Genetic Programming
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic programming
      2. Factorization methods
        Non-negative matrix factorization

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Published In

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

July 2016

1510 pages

ISBN:9781450343237

DOI:10.1145/2908961

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2016

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Poster

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GECCO '16

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SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Companion Paper Acceptance Rate 137 of 381 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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