컬럼샘플링과 Bagging을 활용한 자금시장 거시유동성 이벤트 탐지

초록

Understanding liquidity in the financial market is important for raising funds for financial companies or corporations. Financing meansraising funds necessary for corporate operations. In this study, we propose a liquidity event detection model in the financial marketthat considers the overall state of the financial market and the economy by utilizing macroeconomic variables. In order to understandthe overall state of the financial market and the economy, macroeconomic variables from the United States and Korea are consideredas model input variables. Machine learning models utilizing a large number of macroeconomic variables may exhibit model overfittingdue to the curse of dimensionality. To alleviate this phenomenon, this study conducted a sampling-based column search and utilizedbootstrap aggregation(bagging) to alleviate model variance and overfitting.

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