Do multiple credit ratings affect syndicated loan spreads?

Drago, Danilo; Gallo, Raffaele

doi:10.1016/j.intfin.2018.04.002

Cited by 11 publications

(3 citation statements)

References 47 publications

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“…As shown in Table 7 (columns 6–10), across all types of relationship measures, we observe that relationship lending consistently plays a prominent role in reducing the cost of bank loans, even during economic downturns. These results are consistent with previous findings that support the beneficial role of relationship lending during crisis periods (Beck et al , 2018; Bolton et al , 2016; Drago and Gallo, 2018).…”

Section: Empirical Model Analysis and Resultssupporting

confidence: 93%

“…The advantages of relationship banking have also been tested during severe economic downturns. Beck et al (2018), Bolton et al (2016) and Drago and Gallo (2018) found that, in situations of financial distress, relationship banks continue charging lower interest rates and offering more-favourable loan terms than transactional lenders do. Furthermore, during financially difficult situations, banks assume the more-active governance roles that underline the benefits of firm-bank relationships (Ahn and Choi, 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

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Lending relationships, credit ratings and bank loan spreads: evidence from Indonesian listed companies

Chandera¹,

Setia-Atmaja²

2020

IJMF

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PurposeThis study examines the impact of firm-bank relationships on bank loan spreads and the mitigating role of firm credit ratings on that impact.Design/methodology/approachThe study sample consists of Indonesian publicly listed companies for the period 2006 to 2016; bank-loan data was extracted from the Loan Pricing Corporation Dealscan database. For the degree of firm-bank relationships, the data on each loan is manually computed, using five different methods taken from Bharath et al. (2011) and Fields et al. (2012). All of the regression analyses are controlled for the year fixed effects, heteroscedasticity, and firm-level clustering. To address the endogeneity issues, this study uses several methods, including partitioning the sample, running nearest-neighbour and propensity score matching tests, and using instrumental variables in two-staged least-squares regression models.FindingsIn line with relationship theory and in opposition to the hold-up argument, this study finds that lending relationships reduce bank loan spreads and that the impact is more noticeable among non-rated Indonesian firms. Specifically, each additional unit in the total number of years of a firm-bank relationship and the number of previous loan contracts with the same bank are associated with 7.34 and 9.15 basis-point decreases, respectively, in these loan spreads.Practical implicationsCorporations and banks should maintain close, long-term relationships to reduce the screening and monitoring costs of borrowing. Regulators should create public policies that encourage banks to put more emphasis on relationships in their lending practices, especially in relation to crisis-prone companies.Originality/valueTo the best of the authors’ knowledge, this is the first study to examine the impact of lending relationships on bank loan spreads in Indonesia. The study offers insights on banking relationships in emerging markets with concentrated banking industries, underdeveloped capital markets and prominent business-group affiliations.

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Section: Empirical Model Analysis and Resultssupporting

confidence: 93%

Section: Literature Reviewmentioning

confidence: 99%

Lending relationships, credit ratings and bank loan spreads: evidence from Indonesian listed companies

Chandera¹,

Setia-Atmaja²

2020

IJMF

View full text Add to dashboard Cite

show abstract

“…The rated firms are also significantly influenced. Studies show that credit ratings have an impact on the spreads of syndicated loans (Drago and Gallo, 2018), cash holdings management (Joe and Oh, 2018), premiums paid in mergers and acquisitions (Jory et al, 2016), capital structure adjustments (Huang and Shen, 2015), and the cost of bond issuance (Mählmann, 2009). Therefore, understanding the factors that determine the assignment of specific ratings is of paramount importance.…”

Section: Introductionmentioning

confidence: 99%