Earticle

초록

영어

Business relatedness or similarity is a widely used measure in merger and acquisition studies. The effectiveness of the relatedness measure is central for managerial judgement and performance analysis of diversification decisions. Traditionally the Standard Industrial Classification (SIC Code) is used to represent business similarity, however the search for a more effective metric have led to the creation of alternative industry classifications and business relatedness measures. This paper uses text mining and topic modelling techniques to create new text based similarity metrics. The model is created from texts retrieved from 10-k annual reports submitted to the Securities and Exchange Commission. From these reports two sections of relevance are selected and used as sources for possible business capabilities that form our similarity measure. The new metrics are based on the topic distributions obtained from our Topic Model and on a dictionary of terms that had an impact on the topic model creation. These measures are then compared against the traditionally used SIC Code and a simple cosine similarity comparison of the 10-k text of merging firms.

목차

Abstract
1. Introduction
2. Product Capability and Managerial Capability
3. Data and Methodology
3.1 Data
3.2 Topic Modeling and Dictionary
4. Analysis
4.1 Dependent Variable
4.2 Independent Variables
4.3 Control Variables
5. Results
6. Discussion and Conclusion
References

키워드

저자

• Guillermo Valdez [ Candidate of Master Degree Major in Data Science Kookmin University, College of Business Administration ]
• Byounggu Choi [ Kookmin University, College of Business Administration ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2026

• 십진분류

  KDC 325 DDC 658