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Working capital financing and firm performance: a machine learning approach

Author

Listed:
  • Faisal Mahmood

    (Al-Hamd Islamic University)

  • Zahoor Ahmed

    (Bahçeşehir Cyprus University
    ILMA University)

  • Nazim Hussain

    (University of Groningen
    IPAG Paris Business School)

  • Younes Ben-Zaied

    (EDC Paris Business School)

Abstract

Companies always try to balance the risk and return on their investments, finances, and daily operations. This study presents the moderating role of ownership status, company size, and leverage level while investigating the relationship between short-term-borrowings and profitability in six sectors of Chinese firms. Contrary to the prevalent literature, the current study creates master proxies, via principal component analysis, for major analysis. Two machine learning techniques, decision tree regression, and random forest regression algorithms, are compared with the fixed effect model to find the better estimation approach. The findings confirm the existence of an inverted U-shaped relationship between working capital finance and profitability in six sectors except the textile, significantly affected by ownership structure, company size, and leverage level. Various policy implications are suggested for company managers as well as lending organizations.

Suggested Citation

  • Faisal Mahmood & Zahoor Ahmed & Nazim Hussain & Younes Ben-Zaied, 2025. "Working capital financing and firm performance: a machine learning approach," Review of Quantitative Finance and Accounting, Springer, vol. 65(1), pages 71-106, July.
    • Handle: RePEc:kap:rqfnac:v:65:y:2025:i:1:d:10.1007_s11156-023-01185-w
    DOI: 10.1007/s11156-023-01185-w
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    Keywords

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    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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