WORKING CAPITAL POLICY CLUSTERING TECHNIQUE: A CASE STUDY OF LISTED COMPANIES IN THE INDUSTRIAL SECTOR, MAI THAILAND

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Published: Sep 18, 2024
Keywords:
Hierarchical K-means Clustering Working Capital Policy Aggressiveness

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Bhannawat Wanganusorn
Faculty of Business Administration Maejo University
Sirikul Tulasombat
Faculty of Business Administration Maejo University
Ratchaneeya Bangmek
Faculty of Business Administration Maejo University
Thatphong Awirothananon
Faculty of Business Administration Maejo University

Abstract

          Working capital policy (WCP) has an impact on liquidity and profitability management. An aggressive WCP allows businesses to generate profits from short-term capital sources but poses risks in timely short-term debt repayment. Determination of WCP involves various factors. Previous studies found that the risk level of the working capital policy is solely determined by the financial ratio, overlooking other contributing factors. This research aimed to enhance the coverage of risk dimensions in setting the risk level of WCP by introducing a clustering technique to determine the WCP risk level. The study population consisted of 213 companies listed on the Market for Alternative Investment (MAI). A specific sampling method was employed, focusing on companies within the industrial sector registered on the MAI. This study utilized panel data by gathering financial ratio data from the STATA database and executive position data (CEO, President) through the annual report Form 56-2 from the SEC database. Data collection spanned 15 consecutive years, from 2008 to 2022, divided into two periods: the COVID-19 pandemic outbreak and the non-pandemic period, which resulted in a total of 432 observations. The hierarchical clustering analysis technique was utilized, followed by K-means cluster analysis and one-way ANOVA robustness checks. The results revealed that the working capital investment policy (WCIP) could be categorized into three sub-groups: risky, moderate, and conservative. The working capital financing policy (WCFP) could be classified into two sub-groups: risky and conservative. K-means clustering analysis for risk levels identified two groups: a risky and a conservative WCP. One-way ANOVA analysis for group differentiation indicated three sub-groups for both policies.

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How to Cite
Wanganusorn, B. ., Tulasombat, S. ., Bangmek , R., & Awirothananon, T. . (2024). WORKING CAPITAL POLICY CLUSTERING TECHNIQUE: A CASE STUDY OF LISTED COMPANIES IN THE INDUSTRIAL SECTOR, MAI THAILAND. Journal of Liberal Art of Rajamangala University of Technology Suvarnabhumi, 6(3), 713–726. Retrieved from https://so03.tci-thaijo.org/index.php/art/article/view/278009
Issue
Vol. 6 No. 3 (2024): September - December
Section
Research Articles
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