Detecting Money Market Macro Liquidity Event Using Column Sampling and Bagging 


Vol. 14,  No. 3, pp. 172-178, Mar.  2025
https://doi.org/10.3745/TKIPS.2025.14.3.172


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  Abstract

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

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[IEEE Style]

J. G. Ahn and H. kang, "Detecting Money Market Macro Liquidity Event Using Column Sampling and Bagging," The Transactions of the Korea Information Processing Society, vol. 14, no. 3, pp. 172-178, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.3.172.

[ACM Style]

Jun Gyu Ahn and Hyoun-goo kang. 2025. Detecting Money Market Macro Liquidity Event Using Column Sampling and Bagging. The Transactions of the Korea Information Processing Society, 14, 3, (2025), 172-178. DOI: https://doi.org/10.3745/TKIPS.2025.14.3.172.

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