Learning value-added information of asset management from analyst reports through text mining

Satoru Takahashi, Masakazu Takahashi, Hiroshi Takahashi, Kazuhiko Tsuda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Text mining, one of the emerging fields of data mining, aims at acquiring useful knowledge from text data. In the asset management in finance task domain, although there exist various text data like accounting settlement or analysts' reports, few research and development have been conducted. In this paper, we will explore the feasibility to extract valuable knowledge for asset management through text mining using analyst reports as text data. We will analyze the relationship between text data and numerical data. From empirical study on the practical data, we have confirmed the effectiveness: (1) the extracted keywords are influential to the stock prices, (2) such information is more effective to the large-cap stocks, and (3) such keyword information become more valuable by using numerical information together.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
PublisherSpringer Verlag
Pages785-791
Number of pages7
ISBN (Print)354028897X, 9783540288978
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia
Duration: 2005 Sept 142005 Sept 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3684 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
Country/TerritoryAustralia
CityMelbourne
Period05/9/1405/9/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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