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handle: 10230/48550 , 10230/45004
This paper studies bank learning through repeated interactions with borrowers from a new perspective. To understand learning by lending, we adapt a methodology from labor economics to analyze how loan contract terms evolve as banks acquire new information about borrowers. We construct “proxy” variables for this information using data from borrowers’ out-of-sample, future credit performance. Due to the timing of their construction, banks could not have used these variables directly to price loans. We nonetheless find that these proxies increasingly predict loan prices as relationships progress, even after controlling for possible omitted variable bias. Our methodology provides strong evidence that: (a) bank learning affects loan prices, and (b) relationship benefits are heterogeneous. In particular, higher quality borrowers face differentially lower spreads as their relationship with lenders develop – and banks learn about their quality – while lower quality borrowers see loan prices increase and their loan amounts fall. We further find suggestive evidence that banks incorporate CEO-specific information into loan prices.
Includes supplementary materials for the online appendix
Information acquisition, Relationship lending, Banks, Learning, Syndicated loans
Information acquisition, Relationship lending, Banks, Learning, Syndicated loans
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 50 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |