Earticle

초록

영어

Earnings management is a practice where managers manipulate financial statements to benefit their interests. This practice has gained attention from academics, regulators, and industry professionals. Previous models have attempted to identify earnings management activities, but they have been ineffective in real practice. This study introduces a novel approach using the Genetic Algorithm (GA), a heuristic search technique based on natural evolution. GA generates easy-to- interpret rules and can analyze large amounts of financial data, detecting patterns that might not be apparent through traditional methods. The proposed GA model outperforms other benchmark models, demonstrating better predictive accuracy. This research aims to bridge the perceptual gaps between academics and practitioners regarding earnings management and contribute to a more comprehensive understanding of this critical issue in financial reporting.

목차

Abstract
Introduction
Literature Review
Definition of Earnings management
Detecting earnings management from an academic prospective
Detecting earnings management from a practical prospective
Research Methodology
Variable selection
Data selection
Genetic Algorithm
Benchmark models
Results
Conclusions

키워드

저자

• HUYNH THIEN TRANG [ 부산대학교 경영대학 ]
• MYOUNG-JONG KIM [ 부산대학교 경영대학 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2026

• 십진분류

  KDC 325 DDC 658