Why business credit information sharing leads to better lending decisions

__Abstract__ Bad loans are made in boom times. Good loans are made in recessionary times. Lenders such as suppliers who provide trade credit or banks would be well advised to remember this simple dictum whenever they are approached for credit by a borrower not entirely familiar to them

Soft information matters in SME lending

Loan data from small and medium-sized enterprises (SMEs) has shown that such positive attributes as good management skills and character – so-called ‘soft’ facts – can improve a borrower’s bargaining power with their bank and thus loan terms

Bargaining power and information in SME lending

Small- and medium-sized enterprises (SMEs) are informationally opaque and bank dependent. In SME lending, banks largely rely on soft information, because the scale and scope of hard information are limited. We analyze whether and how hard and soft information affects the borrower's bargaining power vis-à-vis its bank. We use the fact that, for a given credit rating, certain borrowers obtain better loan terms than others to define measures of relative bargaining power. Using SME loan data from the USA and Germany, we find that more favorable soft information (management skills and character) increases borrower bargaining power. We also show that more favorable soft than hard information improves borrower bargaining power. The results are not driven by manipulation or statistical limitations of the credit ratings. Our study suggests that soft information represents an important and direct determinant of borrower bargaining power, affecting the outcomes of the loan contracting process

The Role of Banks in SME Finance

__Abstract__ Banks play a crucial role for the financing of small and medium-sized enterprises (SMEs). SMEs represent a large fraction of all firms in many economies and contribute significantly to employment and growth. But, SMEs are more informationally opaque, more risky, more financially constrained, and more bank-dependent than large firms, which creates serious challenges in SME finance. In this inaugural address, I focus on lending technologies to cope with key challenges in SME finance. I present evidence from two recent empirical studies. The first conclusion is that relationship lending works. Applying meta-analysis in a cross-country context, we show that, on average, borrowers benefit from relationship lending. SMEs obtain more credit and/or lower loan rates under relationship lending. Furthermore, bank competition makes benefits for borrowers more likely. The second conclusion is that trade credit has limited scope to replace bank debt when the latter is subject to a shock. SMEs in Europe have countered a shock to their bank debt to some extent with trade credit. However, substitution has become increasingly difficult during the financial crisis and was only possible for a subset of firms: the ones with better credit quality and intermediate financial constraints. Overall, a comprehensive understanding of lending technologies such as relationship lending and trade credit is critical for lenders, borrowers, and policymakers to ensure the proper functioning of SME finance

Do bank bailouts have a silver lining?

__Abstract__ Much criticism was levelled at the USA’s Troubled Asset Relief Program (TARP) at the time it was announced in the autumn of 2008. Many of its opponents argued that not a penny of taxpayer money should have been spent on shoring up US banks. But were they right

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Credit Rationing with Symmetric Information

Without denying the importance of asymmetric information, this article purports the view that credit rationing may also originate from a lender's inability to classify loan applicants in proper risk categories. This effect is particularly strong when novel technologies are involved. Furthermore, its relevance may increase with the importance assigned to internal rating systems by the Basel accord. This article presents a measure of the inadequacy of a lender's classification criteria to the qualitative features of prospective borrowers. Even without information asymmetries, credit rationing may occur if this quantity reaches too high a value. Furthermore, some general principles are outlined, that may be used by lenders in order to change their classification criteria

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Information in CDS Spreads

We investigate how public and private information drives corporate CDS spreads before rating announcements. We find that CDS spreads of firms with higher news intensity move significantly earlier and stronger before rating announcements, which can be explained with public information from daily wire news. We also find that private information of banks matters. CDS spread changes are larger for firms with more banks and days with no news but large abnormal CDS spread changes are more frequent before negative announcements than before positive ones. The evidence highlights the important role of CDS in processing public and private credit information