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Article

Related Stocks Selection with Data Collaboration Using Text Mining

1
Department of Systems Innovation, Faculty of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
2
Department of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
3
Quantitative Investment Department, Daiwa Asset Management Co. Ltd., 1-9-1 Marunouchi, Chiyoda-ku, Tokyo 100-6753, Japan
4
Frontier Technologies Research & Consulting Deptartment, Daiwa Institute of Research Ltd., 15-6 Fuyuki, Koto-ku, Tokyo 135-8460, Japan
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the 18th IEEE International Conference on Data Mining Workshops (ICDMW 2018), Singapore, 17–20 November 2018.
Information 2019, 10(3), 102; https://doi.org/10.3390/info10030102
Received: 23 January 2019 / Revised: 17 February 2019 / Accepted: 4 March 2019 / Published: 7 March 2019
(This article belongs to the Special Issue MoDAT: Designing the Market of Data)
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. View Full-Text
Keywords: text mining; mutual fund; financial stocks; natural language processing text mining; mutual fund; financial stocks; natural language processing
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MDPI and ACS Style

Hirano, M.; Sakaji, H.; Kimura, S.; Izumi, K.; Matsushima, H.; Nagao, S.; Kato, A. Related Stocks Selection with Data Collaboration Using Text Mining. Information 2019, 10, 102. https://doi.org/10.3390/info10030102

AMA Style

Hirano M, Sakaji H, Kimura S, Izumi K, Matsushima H, Nagao S, Kato A. Related Stocks Selection with Data Collaboration Using Text Mining. Information. 2019; 10(3):102. https://doi.org/10.3390/info10030102

Chicago/Turabian Style

Hirano, Masanori; Sakaji, Hiroki; Kimura, Shoko; Izumi, Kiyoshi; Matsushima, Hiroyasu; Nagao, Shintaro; Kato, Atsuo. 2019. "Related Stocks Selection with Data Collaboration Using Text Mining" Information 10, no. 3: 102. https://doi.org/10.3390/info10030102

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