Ugli, Yusupov Komaliddin Bakhtiyor
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Sector-Specific Practices of Account Receivables in the Shanghai Stock Market Fayzieva, Munisa; Sofia, Alfira; Prawira, Ida Farida Adi; Ugli, Yusupov Komaliddin Bakhtiyor
Journal of Islamic Contemporary Accounting and Business Vol. 3 No. 2 (2025): JICAB
Publisher : Tazkia Islamic University College

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30993/jicab.v3i1.507

Abstract

This study investigates the relationship between Price Close and Accounts Receivable (A/R) across five economic sectors: Technology, Consumer Cyclicals, Healthcare, Basic Materials, and Energy. A 2023 dataset of sector-specific financial metrics was analyzed using regression techniques to assess the impact of stock price movements on receivables management, with sector-specific coefficients calculated to explore distinct credit practices across industries. The results show a moderate positive correlation (r = 0.522) between Price Close and A/R, indicating that higher stock prices generally correspond with larger receivable balances. Regression analysis reveals that the Energy sector exhibits the most substantial coefficient ($290.42 million), reflecting sensitivity to market valuation. In comparison, the Technology sector shows a $117.0 million coefficient, tied to growth-oriented credit policies. The aggregate model explains 27.27% of the variance in A/R, emphasizing the sector-specific nature of this financial relationship. The findings enrich our understanding of how stock market performance influences corporate credit strategies and provide actionable insights for stakeholders in managing credit risks and aligning financial strategies with market conditions. This research uniquely emphasizes sector-specific variations in the linkage between stock prices and receivables management, offering a nuanced perspective on financial behavior and underlining the importance of contextualizing financial metrics within industry-specific frameworks. Future studies should expand the dataset and incorporate additional variables to comprehensively capture external influences.