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This article estimates the loan spread equation taking into account the endogenous matching between banks and firms in the loan market. To overcome the endogeneity problem, I supplement the loan spread equation with a two-sided matching model and estimate them jointly. Bayesian inference is feasible using a Gibbs sampling algorithm that performs Markov chain Monte Carlo (MCMC) simulations. I find that medium-sized banks and firms tend to be the most attractive partners, and that liquidity is also a consideration in choosing partners. Furthermore, banks with higher monitoring ability charge higher spreads, and firms that are more leveraged or less liquid are charged higher spreads.

Prior research suggests that given the legal environment in the U.S., smaller syndicates with fewer lead banks should represent “best practices” to promote efficient monitoring and ease of renegotiation. Such syndicates should be associated with lower loan spreads. Controlling for other influences on loan pricing, we conduct tests of this proposition drawing on data from DealScan, Compustat and Federal Reserve Call Reports for U.S. loans between 1988 and 1999. Consistent with our hypothesis, the number of lead lenders is shown to have a significant positive influence on loan yield spreads.

If banks solve an inter-temporal problem under adverse selection and moral hazard, then bank specific factors, regulatory and supervisory features, market structure, and macroeconomic factors can be expected to affect banks’ loan interest rates and their spread over deposit interest rates. To examine interest rate pass-through for Indian banks in a period following extensive financial reform, after controlling for all these factors, we estimate the determinants of commercial banks’ loan pricing decisions, using the dynamic panel data methodology with annual data for a sample of 33 banks over the period 1996–2012. Results show commercial banks consider several factors apart from the policy rate. This limits policy pass-through. More competition reduces policy pass-through by decreasing the loan rate as well as spreads. If managerial efficiency is high then an increase in competition increases the policy pass-through and the vice-versa. Reform has had mixed effects, while managerial inefficiency raised rates and spreads, product diversification reduced both. Costs of deposits are passed on to loan rates. Regulatory requirements raise loan rates and spreads.

We extend Diamond's (1989, 1991) life-cycle hypothesis to posit that, once they reach the stage of bank borrowing, firms begin with prime loans and evolve toward borrowing more cheaply at LIBOR as they grow larger, less risky and less characterized by asymmetric information. We conduct multinomial logit regressions to explain firms’ membership in one of three groups: prime only, prime and LIBOR, and LIBOR. We also examine spreads over prime and LIBOR and find that loans set up to allow borrowing at prime carry higher spreads than those allowing borrowing at LIBOR. Both sets of tests support the life-cycle hypothesis.

Peer-to-Peer (P2P) lending, which makes borrowers and investors meet directly through online platforms bypassing traditional financial institutions, is an emerging financing market after the traditional financial institutions crushed during the global financial crisis from 2007 to 2009. P2P lending platforms meet the credit demand more efficiently and play a vital role for the credit market and economic activity. This study sheds light on whether the credit spread of P2P lending is well predictive of economic activity compared to the bond credit spread which has been fully investigated in prior studies. Our findings show that the P2P credit spread performs similarly in predicting the economic activity as bond credit spread only during the financial crisis. However, the predictive power of P2P credit spread becomes inverse during the noncrisis periods since P2P lending platforms provide an alternative and easier financing channel to individuals who hardly borrow money for refinancing from traditional financial institutions. This study highlights the alternative role of P2P lending platform in financing and provides the evidence of different predictive powers of P2P credit spread on economic activity in different time periods.