Inspecting the Latent Space of Stock Market Data with Genetic Programming

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@InProceedings{Ha:2016:GECCOcomp,
  author =       "Sungjoo Ha and Sangyeop Lee and Byung-Ro Moon",
  title =        "Inspecting the Latent Space of Stock Market Data with
                 Genetic Programming",
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  pages =        "63--64",
  keywords =     "genetic algorithms, genetic programming: Poster",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4323-7",
  DOI =          "doi:10.1145/2908961.2909004",
  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.",
  notes =        "Distributed at GECCO-2016.",
}

Genetic Programming entries for Sungjoo Ha Sangyeop Lee Byung-Ro Moon

Citations