Abstract
This chapter uses data from a survey conducted by the Organization for Small and Medium Enterprises and Regional Innovation, Japan (SME Support , Japan) to explore soft information factors ’ effects upon lender performance in competitive regional markets, especially as it concerns small and medium-sized/regional financial institutions. In the previous chapter, the authors extracted three latent factors: organizational systems, networks or alliances/partnerships, and business and management leadership. This chapter explores which type of soft information factor that impacts lender performance—profitability and bad loan ratios in particular—by multivariate analyses; and also determines how any combination of soft information factors contributes to lender performance. Further, this chapter investigates soft information factors ’ potent influence on lender performance in the face of interbank competition . The authors’ multivariate models include all the possible control variables that might influence lender performance, including bank- and locally specific variables. Finally, this chapter conducts robustness tests to evaluate the multivariate models’ analytical quality.
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Notes
- 1.
This book’s regression model involves multiple independent variables, and thus, the authors calculated the variance inflation factor (VIF) to ascertain whether the regression model has a multi-collinearity problem. This phenomenon involves one predictor variable that can be linearly and substantially predicted from the other variables in the multivariate regression. If a multi-collinearity problem arises, the multiple regression model’s coefficient estimates may change erratically in response to small changes in the model or the data. The VIF quantifies the multi-collinearity’s severity in an ordinary least squares regression analysis. Unfortunately, several rules associated with VIF are regarded as a sign of severe or serious multi-collinearity, but the “rule of 10” is the most common (O’Brien 2007). When the VIF exceeds 10, the rule of 10 is interpreted as casting doubt on the regression results. In this chapter’s regression models, all the independent variables’ VIFs were less than 7, which is acceptable in accord with the rule of 10. Consequently, the authors concluded that none of the regression models exhibit a severe multi-collinearity problem.
- 2.
The authors’ multivariate models involve all control variables that might influence banks’ loan performance to exclude the possibility of covariation between the dependent and missing control variables. This ensures unbiased and consistent estimates in the regression.
- 3.
Section 2.4 provides definitions of the traditional “competition-fragility” and modern “competition-stability” perspectives.
- 4.
The authors divided the HHI into three groups, and a dummy variable with a value of one was assigned to the bottom tercile group, or high interbank competition (the dummy variable equals one if in the bottom tercile group, and zero otherwise).
- 5.
The extended sample periods are followed by the action program’s 2003 introduction in Japan. This program anticipated that small/medium-sized and regional financial institutions would evaluate companies’ future cash flows using soft information, and thus, enhance and strengthen their credit risk assessment capabilities in the fiercely competitive lending market (see Sect. 1.1).
References
Berger, A. N., De Young, R., & Udell, G. F. (2001). Efficiency barriers to the consolidation of the European financial services industry. European Financial Management, 7(1), 117–130.
Berger, A. N., Klapper, L. F., & Turk-Ariss, R. (2009). Bank competition and financial stability. Journal of Financial Services Research, 35(2), 99–118.
Berger, A. N., Miller, H., Petersen, M. A., Rajan, R. G., & Stein, J. C. (2005). Does function follow organizational form? Evidene from the lending practices of large and small banks. Journal of Financial Economics, 76(2), 237–269.
Bharath, S., Dahiya, S., Saunders, A., & Srinivasan, A. (2007). So what do I get? The bank’s view of lending relationships. Journal of Financial Economics, 85(2), 368–419.
Bofondi, M., & Gobbi, G. (2006). Informational barriers to entry into credit markets. Review of Finance, 10(1), 39–67.
Bowen, H. P., & Wiersema, M. F. (1999). Matching method to paradigm in strategy research: Limitations of cross-sectional analysis and some methodological alternatives. Strategic Management Journal, 20(7), 625–636.
Carter, D. A., & McNulty, J. E. (2005). Deregulation, technological change, and the business-lending performance of large and small banks. Journal of Banking & Finance, 29(5), 1113–1130.
Carter, D. A., McNulty, J. E., & Verbrugge, J. A. (2004). Do small banks have an advantage in lending? An examination of risk-adjusted yields on business loans at large and small banks. Journal of Financial Services Research, 25(2–3), 233–252.
Castanias, R. P., & Helfat, C. E. (1991). Managerial resources and rents. Journal of Management, 17(1), 155–171.
Day, D. V., & Lord, R. G. (1988). Executive leadership and organizational performance: Suggestions for a new theory and methodology. Journal of Management, 14(3), 453–464.
Degryse, H., & Ongena, S. (2005). Distance, lending relationships, and competition. The Journal of Finance, 60(1), 231–266.
Degryse, H., & Ongena, S. (2007). The impact of competition on bank orientation. Journal of Financial Intermediation, 16(3), 399–424.
Gulati, R., Nohria, N., & Zaheer, A. (2000). Strategic networks. Strategic Management Journal, 21(3), 203–215.
Ichikawa, M., & Konishi, S. (1995). Application of the bootstrap methods in factor analysis. Psychometrika, 60(1), 77–93.
Inkpen, A. C., & Tsang, E. W. K. (2005). Social capital, networks, and knowledge transfer. The Academy of Management Review, 30(1), 146–165.
Jensen, M. C., & Meckling, W. H. (1996). Specific and general knowledge, and organizational structure. In P. S. Myers (Ed.), Knowledge management and organizational design (pp. 17–38). Boston: Butterworth-Heinemann.
Jimènez, G., & Saurina, J. (2004). Collateral, type of lender and relationship banking as determinants of credit risk. Journal of Banking & Finance, 28(9), 2191–2212.
Mamatzakis, E., Matousek, R., & Vu, A. N. (2016). What is the impact of bankrupt and restructured loans on Japanese bank efficiency? Journal of Banking & Finance, 72, S187–S202.
O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690.
Podolny, J. M., & Page, K. L. (1998). Network forms of organization. Annual Review of Sociology, 24(1), 57–76.
Porter, M. E. (1991). Towards a dynamic theory of strategy. Strategic Management Journal, 12(S2), 95–117.
van Praag, B. M. S., Frijters, P., & Ferrer-i-Carbonellac, A. (2003). The anatomy of subjective well-being. Journal of Economic Behavior & Organization, 51(1), 29–49.
Radosevic, S. (1999). International technology transfer and catch-up in economic development. Cheltenham: Edward Elgar Publishing.
Stiroh, K. J., & Rumble, A. (2006). The dark side of diversification: The case of US financial holding companies. Journal of Banking & Finance, 30(8), 2131–2161.
Teece, D. J. (2000). Strategies for managing knowledge assets: The role of firm structure and industrial context. Long Range Planning, 33(1), 35–54.
Wooldridge, J. M. (2015). Introductory econometrics: A modern approach (6th ed.). Boston: Cengage Learning.
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Appendices
Appendix 4.1: Interbank Competition and Loan Performance
The multivariable regressions illustrated in Panel A of Table 4.3 consistently report the HHI’s positive effect on loan performance . In the full model, the HHI’s coefficient is 1.2083 at the 1% significance level. Figure 4.1 illustrates the linear relationship between the HHI and loan profitability, which has been adjusted for any covariation with other control variables in the full model, as displayed in the aforementioned Panel A of Table 4.3.
The linear relationship between the HHI and loan profitability
Appendix 4.2: Interbank Competition and the Non-performing Loan Ratio
The multivariable regressions noted in Table 4.4, both Panel A and Panel B consistently report the HHI’s square terms have a negative effect on bad loan ratios. The full model’s HHI coefficient is -78.8006 at the 5% significance level, illustrated in the sixth column from Panel A in Table 4.4. Figure 4.2 depicts an inverse U-shaped relationship, obtained from the regression model displayed in the sixth column from Panel A in Table 4.4. The curve suggests that lenders tend to decrease their thresholds of creditworthiness as interbank competition becomes less severe, resulting in high bad loan ratios when they are located in the local area with an HHI less than 0.218, consistent with the competition-stability perspective. Alternatively, the curve suggests that lenders tend to assume more credit risk as they confront increasing competition, resulting in high bad loan ratios when they are located in the local area with HHIs greater than 0.218, which is consistent with the competition-fragility perspective.
The inverse U-shaped relationship between the HHI and bad loan ratio
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Yosano, T., Nakaoka, T. (2019). The Influence of Using Soft Information on Lender Performance in Competitive Local Markets: An Empirical Analysis. In: Utilization of Soft Information on Bank Performance. SpringerBriefs in Economics(). Springer, Singapore. https://doi.org/10.1007/978-981-13-8472-1_4
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