An Efficient Learning System for Knowledge of Asset Management

Satoru Takahashi, Hiroshi Takahashi, Kazuhiko Tsuda

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper examines if it is possible to obtain valuable knowledge for asset management by performing text mining on an enormous volume of analyst reports. Analyst reports are the evaluation reports of firms that are published by securities analysts. These reports describe the business conditions of firms mainly with a large amount of text information. However, it is impossible for a human being to read and understand all of the reports within a limited amount of time. To address this problem, we extract information from analyst reports automatically using text mining methods and analyze the influences of the reports. As a result of analyses, we confirm that the analyst reports contain valuable information that affect to stock prices. We also find that the stock prices react to the information before the report is published, which indicates that analysts are affected by the opinions of the other analysts.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMircea Gh. Negoita, Robert J. Howlett, Lakhmi C. Jain
PublisherSpringer Verlag
Pages494-500
Number of pages7
ISBN (Print)9783540301325
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3213
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'An Efficient Learning System for Knowledge of Asset Management'. Together they form a unique fingerprint.

  • Cite this

    Takahashi, S., Takahashi, H., & Tsuda, K. (2004). An Efficient Learning System for Knowledge of Asset Management. In M. G. Negoita, R. J. Howlett, & L. C. Jain (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 494-500). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3213). Springer Verlag. https://doi.org/10.1007/978-3-540-30132-5_70