Next Article in Journal
A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR
Next Article in Special Issue
Matrix-Based Method for Inferring Elements in Data Attributes Using a Vector Space Model
Previous Article in Journal
“Indirect” Information: The Debate on Testimony in Social Epistemology and Its Role in the Game of “Giving and Asking for Reasons”
Article Menu

Export Article

Open AccessArticle

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)
  |  
PDF [341 KB, uploaded 7 March 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. View Full-Text
Keywords: text mining; mutual fund; financial stocks; natural language processing text mining; mutual fund; financial stocks; natural language processing
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top