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Related Stocks Selection with Data Collaboration Using Text Mining

Masanori HIRANO, Hiroki SAKAJI, Shoko KIMURA, Kiyoshi IZUMI, Hiroyasu MATSUSHIMA, Shintaro NAGAO, Atsuo KATO

Information, MDPI, vol.10, no.3, e102, 2019


Abstract

We propose an extended scheme for selecting related stocks for themed mutual funds. This scheme was designed to support fund managers who are building themed mutual funds. In our preliminary experiments, building a themed mutual fund was found to be quite difficult. Our scheme is a type of natural language processing method and based on words extracted according to their similarity to a theme using word2vec and our unique similarity based on co-occurrence in company information. We used data including investor relations and official websites as company information data. We also conducted several other experiments, including hyperparameter tuning, in our scheme. The scheme achieved a 172% higher F1 score and 21% higher accuracy than a standard method. Our research also showed the possibility that official websites are not necessary for our scheme, contrary to our preliminary experiments for assessing data collaboration.

Keywords

text mining; mutual fund; financial stocks; natural language processing;

doi

10.3390/info10030102


bibtex

@journal{Hirano2019-information,
  title={{Related Stocks Selection with Data Collaboration Using Text Mining}},
  autor={Masanori HIRANO and Hiroki SAKAJI and Shoko KIMURA and Kiyoshi IZUMI and Hiroyasu MATSUSHIMA and Shintaro NAGAO and Atsuo KATO},
  journal={Information},
  issn={2078-2489},
  volume={10},
  number={3},
  pages={e102},
  publisher={MDPI},
  doi={10.3390/info10030102},
  year={2019}
}