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Soft Information and Small Business Lending

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  • Yehning Chen
  • Rachel Huang
  • John Tsai
  • Larry Tzeng

Abstract

Using data from a Taiwanese finance company, this paper empirically investigates the value of soft information, information that requires the subjective interpretation by the loan officers who collect it and cannot be credibly transmitted to others, for making small business loans. It finds that the use of soft information significantly improves the power of default prediction models. It also identifies the types of soft information that are helpful for predicting loan defaults. In addition, it shows that borrowers with more favorable soft information enjoy lower interest rates. These results imply that soft information is important for small business lending. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Yehning Chen & Rachel Huang & John Tsai & Larry Tzeng, 2015. "Soft Information and Small Business Lending," Journal of Financial Services Research, Springer;Western Finance Association, vol. 47(1), pages 115-133, February.
    • Handle: RePEc:kap:jfsres:v:47:y:2015:i:1:p:115-133
    DOI: 10.1007/s10693-013-0187-x
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    Cited by:

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    3. Carmen Gallucci & Rosalia Santullli & Michele Modina & Vincenzo Formisano, 2023. "Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(3), pages 873-892, September.
    4. Boubakri, Narjess & Cao, Zhongyu & El Ghoul, Sadok & Guedhami, Omrane & Li, Xinming, 2023. "National culture and bank liquidity creation," Journal of Financial Stability, Elsevier, vol. 64(C).
    5. Jauling Tseng, 2021. "How do finance companies' advantages affect competitive strategies in short‐ and intermediate‐term loan markets? A theoretical analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4295-4302, July.
    6. Durguner, Sena, 2017. "Do borrower-lender relationships still matter for small business loans?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 98-118.
    7. Wang, Tong & Zhao, Sheng & Zhou, Mengqiu, 2022. "Does soft information in expert ratings curb information asymmetry? Evidence from crowdfunding and early transaction phases of Initial Coin offerings," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    8. Jing Gu & Junyao Wang & Yang Yang & Zeshui Xu, 2019. "Credit Line Models for Supply Chain Enterprises with Channel Background and Soft Information," Sustainability, MDPI, vol. 11(10), pages 1-20, May.
    9. Ge Gao & Hongxin Wang & Pengbin Gao, 2021. "Establishing a Credit Risk Evaluation System for SMEs Using the Soft Voting Fusion Model," Risks, MDPI, vol. 9(11), pages 1-12, November.
    10. Kim, Kevin N. & Katchova, Ani, 2022. "Determinants of Community & Agricultural Bank Consolidations: A Rare Event Study," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322375, Agricultural and Applied Economics Association.
    11. Kevin N. Kim & Ani L. Katchova, 2022. "Agricultural bank acquisitions and postacquisition performance: An examination of the role of shared knowledge," Agribusiness, John Wiley & Sons, Ltd., vol. 38(4), pages 743-770, October.

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    More about this item

    Keywords

    Soft information; Small business lending; Default prediction; Credit scoring; G21; G33;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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