Earticle

초록

영어

Industry classification has been one of the crucial issues in financial analysis. However, classical industry classification systems have several limitations. Several studies have been progressed to overcome the limitations by using the text information that firms use to describe their business process and products. In this paper, we propose an industry classification methodology based on their business descriptions by reducing high dimensions using autoencoder to avoid a high dimensionality problem in vector space. The main contribution of this paper is first, we overcome the limitation of cosine similarity measure where the word vector is large and highly sparse by reducing the dimension of word vector utilizing the autoencoder. Second, we are able to visualize the relative industry relation of the firms based on the lower dimensional information extracted from the business description text. The relative location can also describe the industry-level relationship as well as the position of individual firms which were originally involved in conflicting assignment problem in terms of the classical classification scheme.

목차

Abstract
 1. Introduction
 2. Data (Business descriptions of the 10-K annual report)
 3. Methodology
  3.1. Bag of words representation
  3.2. Dimensionality reduction by using the autoencoder
  3.3. Industry classification by the spherical k-means clustering
 4. Result
 5. Conclusion
 References

저자

• Kyounghun Bae [ Hanyang University ]
• Daejin Kim [ Ulsan National Institute of Science and Technology ]
• Rocku Oh [ Ulsan National Institute of Science and Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국재무학회 학술대회

• 간기

  부정기

• 수록기간

  2006~2024

• 십진분류

  KDC 325 DDC 330