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General relationship lending thoughts considering information asymmetries

Similar to syndicate structural discussions, relationship lending in general has been a widely studied topic that had led to mixed intellectual approaches and has produced diverse conclusions. Relationship lending theory and research is essentially grounded on assumptions regarding informational frictions in the sense that lending relationships might be beneficial for financiers due to their unique ability to mitigate those asymmetries.88 _{Extant literature}

measures a lender-borrower-relationship-deepness by the history of previous interactions,89

distance (proximity), exclusivity, and cross-product synergies. As early, information-based, financial intermediation theory suggests, banks produce private information via due diligence efforts to mitigate adverse selection prior to making loan decisions (Boot & Thakor, 2000). Further, financiers constantly monitor borrowers to be protected against borrower moral hazard (Diamond, 1984). Aided by repeated interactions with the borrower and the deepening of the lender-borrower relationship, a relationship lender constantly produces reusable, proprietary information leading to an ex post information monopoly, which de novo

uninformed outside lenders not share. According to Diamond (1991), a bank’s costs to conduct due diligence are a decreasing function of relationship length. This leads to an adverse selection issue for these non-lenders and might deter competition (Sharpe, 1990), as Figure 6 exemplifies.

Figure 6. Information-based relationship lending model.

88 _{See sections 2.3 and 2.10.1.}

89 _{For example, via the number of previous loan transactions, length of relationship, total amounts loaned to a client over} time.

Logically, as relationships deepen over time, information asymmetries shrink and the costs of information production drops (Hainz & Wiegand, 2013). In other words, relationship lending leads to scope economies or so-called information rents in mainly soft and private information production.

According to Udell (2008), relationship lending can be defined as a lending technique that predominantly depends on non-quantifiable soft information. In contrast, transaction-based

techniques, such as financial statement lending or asset-based lending, mostly rely on hard and quantifiable information, such as financial data. Hence, relationship lending only creates value if severe information asymmetries exist.

Thus, a huge strand of literature builds on these theoretical ideas and wonders if the presented cost-savings are (partly) shared with borrowers and result in more favourable loan contract terms in general and lower pricings in particular. In other words, loan pricing can either be relatively high or low if relationships were deep.90 _{This empirical question is of interest}

especially regarding bilateral lending relationships in the small business context, where (soft) information production is generally more costly and valuable (Berger & Udell, 1995; Grunert & Norden, 2012; Udell). It is also of interest for large syndicated loans, where participant banks91 _{expect a relationship lead lender to perform a superior ex ante due diligence and an ex} post monitoring compared to a possible non-relationship lead lender. Hence, participants might subsequently accept lower pricings.

In that context, it important to recall the original definition of syndicated lending, according to which only lead arrangers maintain relationships with borrowers, whereas participant lenders are uninformed, arm’s length investors. Under this theoretical constellation, the lead arranger monitors the borrower on behalf of these participants (Sufi, 2007). In that vein, Hale and Santos (2009) stated that syndicated loans in which only one bank acts as lead arranger carry similar financing patterns to bilateral lending agreements.

Extant literature provides competing evidence with some researchers reporting lending relationships to be beneficial (Boot & Thakor, 1994; Bris & Welch, 2005) for borrowers and others not. Findings favouring the latter are asserted to be so-called lock-in- or hold-up-effects

leading to high switching costs. Here, relationship lenders do not pass on the benefits to the

90 _{See Figure 6.}

borrower—being informationally captured92—and, thus, extract rents (Houston & James,

1996; Rajan, 1992; Sharpe, 1990). In other words, “If the borrower seeks to switch to a new funding source, it is pegged as a lemon regardless of its true financial condition” (Santos & Winton, 2008, p. 1,315). Logically, Mattes et al. (2013) stated that these switching costs are a sufficient condition for banks to extract rents. In that context, Kim, Kliger, and Vale (2003) interpreted switching as the related costs of asymmetric information. In summary, one can state, that the overall scholarly debate is about relationship benefits versus possible lock-in- effects under theoretical information asymmetry assumptions.

Relationship lending, information asymmetry, and pricing

Largely, relationship lending literature focuses on small- and medium-sized enterprises, which tend to be opaque by nature and tend to issue external funding by means of bilateral bank loans.93 _{Here, banks need to rely overwhelmingly on soft information by evaluating}

borrowers creditworthiness as scale and scope of hard information are limited compared to large companies (Grunert & Norden, 2012; Ortiz-Molina & Penas, 2008).

2.11.2.1 Syndicated relationship lending and pricing in context of general

theoretical thoughts

A smaller number of inquiries focused on syndicated lending—the heart of my study— whereas others simply lumped bilateral and syndicated loans together, thus risking the dilution of results. One strand of this literature focused on the general discussion of relationship lending’s benefits versus related costs from a borrower’s perspective.

By screening large enterprises, Bharath et al.’s (2011) core research examined the effects of prior lending-relationships on pricing and other non-price-related terms based on a loan set to publicly listed U.S. non-financial firms. Following Boot and Thakor (1994), Bharath et al. (p. 1,195) found that relationship lending leads to lower AISD, thus confirming the hypothesis, “Economies in information production due to the repeated interaction between the same lender and borrower are at least partly reflected in the price of loans”.

92 _{According to Rajan (1992), relationship banks know whether a firm is going to succeed or not, whereas outside de novo} lenders only anticipate this, leading to a demand of higher spreads, resulting in switching costs. In other words, the relationship bank is fully informed and the non-relationship bank completely uniformed.

93 _{Among others, Stein (2014), by examining firms of annual sales levels of €50 million to €150 million, found evidence for} both hold-up and relationship benefit hypothesis. Borrowing costs tend to fall with relationship strength but tend to rise with its duration.

Further, the authors found that informationally opaque borrowers benefit more from relationships, disproving the lock-in hypothesis of, for example, Sharpe (1990) and Rajan (1992), under which opaque borrowers especially should either experience no benefit or, at least, less benefit from relationship lending. Next, in line with Boot and Thakor (2000), Bharath et al. (2011) found that very large borrowers94 _{mark a point at which pricings of}

relationship and non-relationship facilities become indistinguishable. As these very large borrowers are likely to be the most informationally transparent, this finding underlines the assumption that relationship lending is valuable tool for mitigating information frictions like

ex ante adverse selection and ex post moral hazard and loses its value creation potential in

“full” transparency situations. Table 55 summarises the main elements of the Bharath et al. article.

Table 55. Summary table for Bharath et al. (2011).

One might offer the critique that Bharath et al. only focused on stock-listed U.S. firms, for which a high degree of transparency can be expected across the size spectrum. Studying relationship lending phenomena given information asymmetrical assumptions is likely to be more valuable if privately held companies with the potential for severe opacity were included.

94 _{The top 30% ranked by asset size.}

Region/country Borrower type Time frame Research

philosophy Methods applied

Pricing definition

Syndicated loan

data provider Sample size

U.S. Non-financial publicly listed firms 1986-2003 Realism, Positivism Regression analyses AISD Thomson Reuters LPC 21,632 tranches

> Larger borrowers pay lower spreads.

> Lower leverage, higher profitability and higher current ratio are associated with lower spreads. > Longer maturity loans carry lower spreads.

> Secured loans carry higher spreads. > Loans with covenants carry higher spreads.

Findings

> Relationship loans are marked by better spreads and lower collateral, they also tend to be associated with greater debt availability. > Repeated borrowing from the same lender translates into a 10 - 17 bp p.a. lowering of loan spreads.

> This provides evidence that hypothesized economies in information production due to repeated interaction between the same lender and borrower, are at least partly reflected in the price of loans.

> When information opacity of a borrower increases, the observed reduction in the cost of borrowing due to a relationship becomes greater. > Spreads charged for relationship loans and nonrelationship loans become indistinguishable if the borrower was in the top 30 when ranked by asset size. Similar dissipation of relationship benefits occurs if the borrower has a rated public debt or is part of the S&P 500 index. > Past relationships can mitigate syndicate moral hazard issues by serving as a commitment to monitor.

> Relationship loans are less likely to be secured.

Bharath, S. T., Dahiya, S., Saunders, A., & Srinivasan, A. (2011). Lending relationships and loan contract terms. The Review of Financial Studies, 24, 1141-1203.

Primary subject findings

Using indicators such as lending frequency and duration between borrowers, lead arrangers, and participant banks, Gadanecz et al.’s (2012) core study found that participant lenders— especially once borrower opacity becomes more pronounced—require a pricing premium. This is at first view contradictory to the results of Bharath et al. (2011), but is, however, likely to be explained by the fact that Bharath et al. studied large stock-listed firms whereas Gadanecz et al.—in line with my critical remark—did not restrict their sample to stock-listed companies. By counterbalancing these information asymmetries, the subsistence of an external rating makes the pricing premium disappear. Further, even for opaque95 _borrowers,

the authors found that repeat lending-relationships of participant banks with the borrower make them graduate from uninformed to informed lenders willing to accept lower pricings given the diminished degree information asymmetries. These cost benefits resulting from repeated interactions between borrower and participants are not found to be significant for rated borrowers. Table 56 highlights the main elements of Gadanecz et al.

Table 56. Summary table for Gadanecz et al. (2012).

Gadanecz et al. other than Bharath et al. used direct measures of information asymmetries, namely, the number of previous relationships, and did not solely look at the lead arrangers’ relationship with a borrower, but measured the influence of repeated interactions between participants and borrower. Furthermore, their sample is likely to be more eligible to deliver incremental inside into the debate, as Gadanecz et al. did not solely focus on listed companies. On the other hand, a possible drawback to the sample is that the authors used a worldwide and

95 _{Meaning here: non-listed and non-rated.}

Region/country Borrower type Time frame Research

philosophy Methods applied

Pricing definition

Syndicated loan

data provider Sample size

Worldwide Non-financial firms 1993-2006 Realism, Positivism > Regression analyses AISD Dealogic Loanware 5,867 loans Findings

> If participant banks have information inferiority in the syndicate, they demand a higher spread due to information asymmetries between them and the borrower.

> Results demonstrate the bargaining power of participant banks on the pricing of syndicated loans; they have the ability to influence pricing depending on their own information set about the borrower.

> For less opaque borrowers, an experienced and well-known arranger has an impact on the pricing and lowers spread. > Reputation effects on pricing are not significant for opaque borrowers.

> There is a negative relationship between share retained by the lead and the spread (only rated). > For opaque borrowers, there is a positive relationship between retained lead share and the spread. > Arrangers retain a higher share of risky loans, which is more likely for opaque borrowers.

> The certification effect of the credit rating together with the arrangers' reputation is strong enough to lower spreads, whereas the arrangers' reputation by itself does not guarantee lower spreads.

Gadanecz, B., Kara, A., & Molyneux, P. (2012). Asymmetric information among lending syndicate members and the value of repeat lending.

International Financial Markets, Institutions and Money, 22, 913-935.

rather small sample,96 _{which might lead to diluted results, an issue that is only cursorily}

acknowledged in the robustness checks.

2.11.2.2 Syndicated relationship lending and pricing in context of economic

cycles

Another body of literature focused on relationship lending and its impact on credit supply and its terms and conditions in times of financial crises, when banks are supposed to face higher degrees of information asymmetry. In other words, in times of crises, information matters even more that it does during “normal” cycles, and the role of relationship lending is hence likely to become more important in that context as well. Thus, this literature strand somewhat extends my discussion of 2.9, where general macroeconomic conditions and their impact on pricing were thematised.

As enterprises carry higher bankruptcy risks during times of recession and economic turmoil, banks enjoying information advantages might be enabled to then exploit clients even more by setting loan pricings higher than justified by the increased risk of failure alone. Based on survey data for 1,139 firms from the manufacturing sector, Hainz and Wiegand (2013) found that firms using relationship lending were less negatively influenced by the financial crisis around 2008/2009 in general, without finding, however, clear directions regarding the impact on loan pricing specifically.

In their core research, Santos and Winton (2008) compared bank loan pricings for enterprises with access to public debt markets with those that were dependent on banks, especially in a recession context. In line with the hold-up hypothesis, the authors found that raising loan pricings is more pronounced for bank-dependent borrowers than for bank- independent borrowers97 _{during recessions. In other words, borrowing from a relationship}

lender is less attractive in recessions than in non-recessions if the firms in question happened to be relatively opaque. Table 57 summarises the main elements of the Santos and Winton article.

96 _{The loan sample of Gadanecz et al. (2012) is composed as follows: U.S.: 3,762 loans; Asia/Pacific: 1,041 loans; Europe:} 844 loans; Latin America/Caribbean: 132 loans; Africa/Middle East: 88 loans.

Table 57. Summary table for Santos and Winton (2008).

Because macroeconomic shocks increase adverse selection and likewise the lock-in risk for opaque borrowers, Mattes et al. (2013)—in their core study—found that undercapitalised

banks exploited their information advantage over “captured” opaque firms, which face high switching costs. This effect is only observed in times of economic recession. Well-capitalised banks in contrast are not-found to extract such rents. Table 58 summarises the principle elements of Mattes et al.

Region/country Borrower type Time frame Research

philosophy Methods applied

Pricing definition

Syndicated loan

data provider Sample size

U.S. Stock-listed non-

financial firms 1987-2002 Realism, Positivism > Regression analyses Spread over reference rate Thomson Reuters LPC 13,846 tranches

> Spreads are higher for bank-dependent firms than for firms with access to public bond markets. > Older firms, larger firms with more tangible assets pay significantly lower spreads.

> A greater market to book ratio leads to lower spreads.

> Most of the proxies for default risk, profit margin, interest coverage, leverage and Z score have positive impact on spreads. > Term loans have lower spreads than RCFs.

> Bridge loans carry higher spreads than normal term loans. > Takeover purpose has no impact on spreads.

> Guarantees and collateral lead to higher spreads. > Longer maturities lead to lower spreads.

> Number of lenders in the syndicate have no impact on spread. > Larger loans have lower spreads.

> Renewals have lower spreads.

> Firms with high leverage that are bank-dependent pay higher spreads than firms with high leverage that have market access. > Stock market volatility leads to higher spreads.

Findings

> Spreads rise in recessions.

> Spreads in recessions rise by a greater degree for bank-dependent firms. > Evidence suggests that informational hold-up effects do exist.

> Firms that have issued any bond, pay 75bp less than other firms; firms that have issued public bonds, pay 90 bp less than other firms; firms whose most recent bond was publicly issued, pay 102 bp less than other firms.

> During recessions, spreads are on average 20 bp higher than during expansions. Firms that have issued public bonds experience only a 6 bp rise in spreads during a recession, unlike other borrowers who face an increase of 31bp.

Santos, J. A. C., & Winton, A. (2008). Bank loans, bonds, and information monopolies across the business cycle. The Journal of Finance, 63, 1315- 1359.

Primary subject findings

Table 58. Summary table for Mattes et al. (2013).

Like several other studies, Mattes et al. (2013) revealed issues regarding public data availability. The authors started with a sample of 5,063 tranches and, after excluding those with missing information, they were left with only 988 tranches, i.e., roughly 20% of the overall sample.

Relatedly, the core study conducted by Alexandre et al. (2014) analysed whether past relationships and borrower experience in the market for syndicated loans are mitigating elements for deteriorating terms in times of economic turmoil.98 _{The authors found that}

frequent syndicated loan borrowings per se did not mitigate term deteriorations in crises. Banks might perceive an overuse of loans as higher risk.99 _{However, a previous relationship}

with the lead arranger in particular and with the other syndicate members in general leads to more favourable pricings in such an environment. This beneficial tendency is even stronger, when pre- and post-crisis syndicates are identical. Logically, during the financial crisis around 2008/2009, borrower-benefits resulting from relationship lending tend to outweigh possible lock-up effects, which is a finding that contradicts the argumentation of Santos and Winton (2008). Table 59 summarises the main elements of Alexandre et al.

98 _{Here the 2008/2009 financial crisis.}

99 _{Due to the higher indebtedness; higher leverage.}

Region/country Borrower type Time frame Research

philosophy Methods applied

Pricing definition

Syndicated loan

data provider Sample size

UK Non-financial firms 1996-2005 Realism, Positivism > Regression analyses AISD Thomson Reuters LPC 988 tranches

> The smallest borrowers pay the largest spreads. > Largest borrowers pay lowest spreads. > Collateralised loans have higher spreads. > Inclusion of covenants increases loan spreads. > Term loans carry higher spreads.

Findings

> Capital-constraint banks exploit their information monopolies over borrowers with high costs for switching lenders by charging higher loan spreads than their well-capitalized peers (weak bank effect) (only in recessions).

> There are information monopolies that enable weak banks to charge higher spreads to borrowers with high switching costs. > These are mainly driven by external events, such as a recession.