Related Stocks Selection with Data Collaboration Using Text Mining

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 l...

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Bibliographic Details
Published inInformation (Basel) Vol. 10; no. 3; p. 102
Main Authors Hirano, Masanori, Sakaji, Hiroki, Kimura, Shoko, Izumi, Kiyoshi, Matsushima, Hiroyasu, Nagao, Shintaro, Kato, Atsuo
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 07.03.2019
Subjects
Artificial intelligence
Collaboration
Data mining
financial stocks
Human error
Investment advisors
Investments
mutual fund
Natural language processing
Similarity
Stock exchanges
text mining
Websites
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Summary: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.
ISSN:2078-2489
2078-2489
DOI:10.3390/info10030102