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How Banks Price Loans for LBOs: an Empirical Analysis of Spread Determinants *

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Abstract

This paper examines which factors determine the pricing of loans for LBOs, using a worldwide sample of 11,111 loans closed in the 2000–2016 period. Our findings are consistent with the hypotheses that loans for LBOs extended to borrowers in market- versus bank-based financial systems are differently priced, and that law and institutional characteristics are important determinants of spreads for deals closed in market-oriented countries. Despite LBO loan pricing differing significantly in normal versus crisis times, loans extended to borrowers in market-based financial systems have higher spreads than those where banks play a major role. Our results also support the hypothesis of tranching as a mechanism of reducing spreads by completing financial markets and mitigating informational asymmetries. Finally, a robust convex relationship between spread and maturity is found, suggesting higher market competition by banks and investors for standard, medium-term maturities.

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Notes

  1. For further discussion see, e.g., Kaplan and Stein (1993), Nikoskelainen and Wright (2007), Kaplan and Strömberg (2009), and references therein.

  2. According to Esty and Megginson (2003), syndicated loans are pyramids with a few arranging banks (arrangers) at the top and many providing banks (providers) at the bottom.

  3. Extant literature is concentrated particularly upon the determinants of an LBO and also on estimating the value for the shareholders of such an acquisition (Caselli and Gatti 2005).

  4. See Collin-Dufresne et al. (2001), Campbell and Taksler (2003), Titman et al. (2004), Longstaff et al. (2005), Chen et al. (2007), Qian and Strahan (2007); Bae and Goyal (2009); Maskara (2010); Bharath et al. (2011); and Flannery et al. (2012), for examples of this literature.

  5. Kleimeier and Megginson (2000) classify syndicated loans in five categories: project finance loans; corporate control loans (e.g., leveraged buy-outs); capital structure loans; fixed asset-based loans; and general corporate purpose loans.

  6. Syndicated loans can be tranched into heterogeneous loans, usually distributed across lenders with different risk aversion (Maskara 2010).

  7. We examine the impact of targeting firms’ characteristics on the pricing of LBO loans by using a unique dataset of loans and firms’ characteristics carefully hand matched from DealScan and Datastream.

  8. Structured finance is an off-balance-sheet contractual arrangement designed to fund a specified asset, or a segregated pool of assets or cash flow streams, within an SPV incorporated to serve as a separate contracting entity for the transaction parties; e.g., securitization, project finance, LBOs and structured leases (Fabozzi et al. 2006).

  9. Cao et al. (2019) provide evidence supporting that institutional context and legal environment impact value creation in LBOs vis-à-vis non-LBO takeovers.

  10. LBOs’ debt can be classified as senior debt (25–45%), subordinated debt (10–25%) and mezzanine financing, with each category having tranches with different contractual structures and seniority levels.

  11. It is interesting to note that: (i) 4 lead banks (Credit Suisse, Goldman Sachs & Co, Lloyds Bank, and Deutsche Bank AG) are in the top 10 for both financial systems; and (ii) although top lead banks arrange deals across the two financial systems, they typically fall within the home financial system – 77.81% (67.02%) of the deals, by volume, closed in market-based (bank-based) financial systems are also arranged by top lead banks with headquarters in such a system.

  12. We use a reduced-form model along the lines of existing pricing models for corporate bonds (e.g., Campbell and Taksler 2003; Chen et al. 2007; Marques and Pinto 2020) and loans (Carey and Nini 2007; Qian and Strahan 2007; Daniels and Ramirez 2008; Bae and Goyal 2009; Bharath et al. 2011; Lin et al. 2011; Lim et al. 2014).

  13. In unreported univariate statistical analysis, we compare the distribution of variables and find that spread and most of the common pricing characteristics in fact differ significantly, not only between loans closed in bank-based and market-based financial systems but also among loans extended in the pre- versus crisis-period. Therefore, we would expect the impact on pricing to be market- and time-specific.

  14. DealScan defines the AISD as ‘the amount the borrower pays in basis points over LIBOR for each dollar drawn down. It adds the spread of the loan with any annual (or facility) fee paid to the bank group.’

  15. We determine the nationality of the borrower by the country in which the target firm is incorporated.

  16. A term loan tranche is a loan facility for a specified amount, fixed repayment schedule and maturity, and is usually fully funded at origination. A credit line or revolver facility have shorter maturities than term loans and are drawn down at the discretion of the borrower (Lim et al. 2014).

  17. In this section, we do not distinguish between a firm’s attributes because of the significant sample size reduction that it would impose from 11,111 to 2141 observations. We control for acquired firms’ characteristics in section 5.

  18. We also run unreported specifications including year fixed effects instead of the crisis dummy variable, with similar results.

  19. Although not reported, we perform t-tests and reject the null hypothesis of the sum of core variables’ coefficients and those of their interaction with the crisis dummy are equal to zero.

  20. Due to limited space, we do not show the results. However, the results are available from the authors.

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  1. Universidade Católica Portuguesa, Católica Porto Business School and CEGE, Rua Diogo Botelho, 1327, 4169-005, Porto, Portugal

    Paulo P. Alves, M. Ricardo Cunha, Luís K. Pacheco & João M. Pinto

  2. International Centre for Research in Accounting, Lancaster University Management School, Lancaster, UK

    Paulo P. Alves

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  1. Paulo P. Alves
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  2. M. Ricardo Cunha
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* The authors thank Miguel Ferreira, Antonios Kalyvas, Ambrus Kecskes, William Megginson, Álvaro Nascimento, Ricardo Ribeiro, Esperança Tchili, Haluk Ünal, and an anonymous referee for their helpful suggestions. We would also like to thank participants in the FMA 2017 Annual European Conference in Lisbon, the 2017 INFINITI Conference on International Finance in Valencia, and the 7th Accounting and Finance Conference of the Catholic University of Portugal-Porto for helpful comments on earlier drafts. Financial support from Fundação para a Ciência e Tecnologia (through project UID/GES/00731/2019) is gratefully acknowledged.

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Alves, P.P., Cunha, M.R., Pacheco, L.K. et al. How Banks Price Loans for LBOs: an Empirical Analysis of Spread Determinants *. J Financ Serv Res 62, 163–200 (2022). https://doi.org/10.1007/s10693-021-00355-y

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