Banking market concentration and syndicated loan prices

The information is collected from Thomson Global Syndicated Loans Reviews and Bank for International Settlements (BIS).

The lending limits as defined in FDIC law, §32.3, where the maximum size of loan to single borrower is 15% of the bank’s or savings association’s capital and surplus.

Our data and information from FDIC also provide empirical evidence to support such a possibility. For example, FDIC shows that only 2 banks headquartered in Arizona operate interstate banking and branching in another 4 states. These two banks, however, lead syndicated loans for borrowers from 26 states in total.

A lead arranger may face a free-riding problem by participant lenders in information collection and monitoring (Lee and Mullineaux 2004) if participant lenders hold a small portion of loans (9.6% averagely for each participant in our data).

The information on bank market structure from FDIC covers a period since 1994 and the Compustat-DealScan table covers a period until 2012 (Chava and Roberts 2008).

Fees are used to price cancellation and drawdown options and to screen borrowers who possess private information to exercise the fee options by which lenders could learn the likelihood of borrower’s future credit line usage by the combination of loan spread options and commitment fee options (Berg et al. 2015). Commitment fee also enhances bank reputation by keeping its promise, prevents bank from extracting extra rents by intimidating to withhold credit, and weakens the effects of moral hazard. Borrowers have an option to draw on a line of credit and each line of credit provides the borrower with an option to draw at a pre-specified spread. Borrowers would be more likely to draw down their lines of credit when spot market spreads are high (Berg et al. 2015). With different combinations of fee and spread, lender can predict future behavior of borrower. For example, if borrowers choose contracts with low fee and high spread, they are more likely to draw down their credit lines.

We measure market concentration at a state level for three reasons. (1) Banks operating in multiple MSAs usually set uniform-prices which are independent from MSA market concentration (e.g. Heitfield and Prager 2004; Heitfield 1999; Radecki 1998). (2) States still have considerable leeway to decide the rules in governing entry by out-of-state banks since IBBEA (e.g. Johnson and Rice 2008; Rice and Strahan 2010). (3) Due to the size of syndicated loan (averaged at $366 m) and borrower (averaged asset value of $5.02 billion), it is highly likely that syndicated loans are raised ‘distantly’ across county and MSA. Therefore, defining the local market at county or metropolitan area is no longer evident and instead, state boundaries seem to be appropriate for bank market (e.g. Radecki 1998).

We do not have information on foreign banks and only consider U.S banks in participating loan syndication, accounting for 70% of total participant lenders in U.S syndicated loan markets.

We also use endogenous switching regression model corresponding to the possible endogenous selection between same-state lead arranger and out-state lead arranger. The results are consistent with Table 4 and available on request from authors.

The robustness tests for the participant’s bank market are also available from the authors on request.

We also perform endogenous switching regression model to control for the selection basis arising from choices between commercial lead arranger and non-commercial lead arranger. The results are consistent with Table 10 and are available from the authors on request.

In Eq. (1), we use one-year lagged concentration to overcome the possible reverse causality issue.

We also consider loan prices (e.g. spread, overlibor) at time t-1 and our results still hold.