Asia-Pacific Journal of Business (아태비즈니스연구)

KNU The Institute of Management & Economy Research (강원대학교 경영경제연구소)
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Predicting Administrative Issue Designation in KOSDAQ Market Using Machine Learning Techniques

머신러닝을 활용한 코스닥 관리종목지정 예측

  • Received : 2022.05.30
  • Accepted : 2022.06.27
  • Published : 2022.06.30
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Abstract

Purpose - This study aims to develop machine learning models to predict administrative issue designation in KOSDAQ Market using financial data. Design/methodology/approach - Employing four classification techniques including logistic regression, support vector machine, random forest, and gradient boosting to a matched sample of five hundred and thirty-six firms over an eight-year period, the authors develop prediction models and explore the practicality of the models. Findings - The resulting four binary selection models reveal overall satisfactory classification performance in terms of various measures including AUC (area under the receiver operating characteristic curve), accuracy, F1-score, and top quartile lift, while the ensemble models (random forest and gradienct boosting) outperform the others in terms of most measures. Research implications or Originality - Although the assessment of administrative issue potential of firms is critical information to investors and financial institutions, detailed empirical investigation has lagged behind. The current research fills this gap in the literature by proposing parsimonious prediction models based on a few financial variables and validating the applicability of the models.

Keywords

Acknowledgement

본 연구는 한성대학교 학술연구비 지원과제임.

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