Investigation of the latent space of stock market patterns with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Ha:2018:GECCO,
  author =       "Sungjoo Ha and Sangyeop Lee and Byung-Ro Moon",
  title =        "Investigation of the latent space of stock market
                 patterns with genetic programming",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "2018",
  editor =       "Hernan Aguirre and Keiki Takadama and 
                 Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and 
                 Andrew M. Sutton and Satoshi Ono and Francisco Chicano and 
                 Shinichi Shirakawa and Zdenek Vasicek and 
                 Roderich Gross and Andries Engelbrecht and Emma Hart and 
                 Sebastian Risi and Ekart Aniko and Julian Togelius and 
                 Sebastien Verel and Christian Blum and Will Browne and 
                 Yusuke Nojima and Tea Tusar and Qingfu Zhang and 
                 Nikolaus Hansen and Jose Antonio Lozano and 
                 Dirk Thierens and Tian-Li Yu and Juergen Branke and 
                 Yaochu Jin and Sara Silva and Hitoshi Iba and 
                 Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and 
                 Federica Sarro and Giuliano Antoniol and Anne Auger and 
                 Per Kristian Lehre",
  isbn13 =       "978-1-4503-5618-3",
  pages =        "1254--1261",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205455.3205493",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We suggest a use of genetic programming for
                 transformation from a vector space to an understandable
                 graph representation, which is part of a project to
                 inspect the latent space in matrix factorization. Given
                 a relation matrix, we can apply standard techniques
                 such as non-negative matrix factorization to extract
                 low dimensional latent space in vector representation.
                 While the vector representation of the latent space is
                 useful, it is not intuitive and hard to interpret. The
                 transformation with the help of genetic programming
                 allows us to better understand the underlying latent
                 structure. Applying the method in the context of a
                 stock market, we show that it is possible to recover
                 the tree representation of technical patterns from a
                 relation matrix. Leveraging the properties of the
                 vector representations, we are able to find patterns
                 that correspond to cluster centres of technical
                 patterns. We further investigate the geometry of the
                 latent space.",
  notes =        "Also known as \cite{3205493} GECCO-2018 A
                 Recombination of the 27th International Conference on
                 Genetic Algorithms (ICGA-2018) and the 23rd Annual
                 Genetic Programming Conference (GP-2018)",
}

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

Citations