Out-of-Sample Predictability of Economic Efficiency Measures of U.S. Banks: Evidence of Capital Adequacy Requirements

Kekoura Sakouvogui

doi:10.1515/roe-2020-0025

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Out-of-Sample Predictability of Economic Efficiency Measures of U.S. Banks: Evidence of Capital Adequacy Requirements

Kekoura Sakouvogui

Veröffentlicht/Copyright: 2. März 2021

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Aus der Zeitschrift Review of Economics Band 71 Heft 3

Abstract

This paper contributes to the sparse debate on the effect of capital adequacy requirements on banks’ economic efficiency measures. Precisely, we evaluate the out-of-sample predictability of capital adequacy requirements on banks’ economic efficiency measures using Support Vector Regression (SVR) model with Linear, Polynomial and Radial Basis Function kernels and ordinary least squares (OLS) model. This analysis is important because a prediction of economic efficiency measures allows for an untangle view of bank’s progress that is useful for management as it gains a high degree of transparency in the evaluation of future events. Our framework adapts optimization of h-block cross-validation to account for serial correlation of economic variables to produce robust sets of tuning parameters for SVR model. Using a total of 10,380 December quarterly observations of U.S. Commercial and Domestic banks spanning from 2008 through 2019, empirical results show that SVR model provides better benchmarking insights in the evaluation of economic efficiency measures compared to the OLS model. Furthermore, in contrast to previous approaches identifying a single “best” model among competing models, the results of Model Confidence Test suggests that the out-of-sample forecasting confidently identifies superior predictive accuracy of SVR model-based forecasts over OLS model.

Keywords: DEA; economic efficiency measures; out-of-sample predictability; SVR; U.S. banks

JEL classification: C01; C18; C52; Q11

Corresponding author: Kekoura Sakouvogui, PhD, Decennial Statistical Studies Division, United States Census Bureau, Washington, DC20746, USA, E-mail: sakouvoguikekoura@yahoo.com

The views set forth are those of the author and do not indicate concurrence by the United States Census Bureau nor the Department of Commerce. sakouvoguikekoura@yahoo.com.

Financial disclosure
1. Funding: This study received no funding.
Ethical statements
1. Research involving Human Participants and/or Animals: No.
2. Disclosure of potential conflicts of interest: The authors declare that they have no conflict of interest.
3. Informed consent: No informed consent was needed since this study does not Human Participants and/or Animals.

References

Anderson, G. and Audzeyeva, A. (2019). A coherent framework for predicting emerging market credit spreads with support vector regression, finance and economics discussion series-074. Board of Governors of the Federal Reserve System, Washington. https://doi.org/10.17016/FEDS.2019.074.Suche in Google Scholar

Assaf, A., Berger, A., Roman, R., and Tsionas, M. (2019). Does efficiency help banks survive and thrive during financial crises? J. Bank. Finance 106: 445–470. https://doi.org/10.1016/j.jbankfin.2019.07.013.Suche in Google Scholar

Banker, R., Charnes, A., and Cooper, W. (1984). Some models for estimating technical scale inefficiencies in data envelopment analysis. Manag. Sci. 30: 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078.Suche in Google Scholar

Barth, R., Caprio, G., and Levine, R. (2004). Bank regulation and supervision: what works best? J. Financ. Intermediation 13: 205–248. https://doi.org/10.1016/j.jfi.2003.06.002.Suche in Google Scholar

Barth, J., Lin, C., Ma, Y., Seade, J., and Song, M. (2013). Do bank regulation, supervision and monitoring enhance or impede bank efficiency? J. Bank. Finance 37: 2879–2892. https://doi.org/10.1016/j.jbankfin.2013.04.030.Suche in Google Scholar

Berger, N. and Bonaccorsi, E. (2006). Capital structure and firm performance: a new approach to testing agency theory and an application to the banking industry. J. Bank. Finance 30: 1065–1102. https://doi.org/10.1016/j.jbankfin.2005.05.015.Suche in Google Scholar

Brabanter, K.D., Brabanter, J., Suykens, J., and Moor, B. (2011). Kernel regression in the presence of correlated errors. J. Mach. Learn. Res. 12: 1955–1976.Suche in Google Scholar

Burman, P., Chow, E., and Nolan, D. (1994). A cross-validatory method for dependent data. Biometrika 84: 351–358. https://doi.org/10.1093/biomet/81.2.351.Suche in Google Scholar

Carmack, P., Spence, J., Schucany, W., Gunst, R., Lin, Q., and Haley, R. (2009). Forecasting cross validation. J. Comput. Graph. Stat. 18: 879–893. https://doi.org/10.1198/jcgs.2009.07034.Suche in Google Scholar

Casu, B. and Girardone, C. (2002). A comparative study of the cost efficiency of Italian bank conglomerates. Manag. Finance 28: 3–23. https://doi.org/10.1108/03074350210768031.Suche in Google Scholar

Charnes, A., Cooper, W., and Rhodes, E. (1978). Measuring the efficiency of decision-making units. Eur. J. Oper. Res. 2: 429–444. https://doi.org/10.1016/0377-2217(78)90138-8.Suche in Google Scholar

Chortareas, E., Girardone, C., and Ventouri, A. (2012). Bank supervision, regulation, and efficiency: evidence from the European Union. J. Financ. Stabil. 8: 292–302. https://doi.org/10.1016/j.jfs.2011.12.001.Suche in Google Scholar

Choudhury, S., Ghosh, S., Bhattacharya, A., Fernandes, K., and Tiwari, M. (2014). A real time clustering and SVM based price-volatility prediction for optimal trading strategy. Neurocomputing 131: 419–426. https://doi.org/10.1016/j.neucom.2013.10.002.Suche in Google Scholar

Cortes, C. and Vapnik, V. (1995). Support vector networks. Mach. Learn. 20: 273–297. https://doi.org/10.1007/bf00994018.Suche in Google Scholar

Dickey, A., and Fuller, A. (1979). Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74: 427–431.10.1080/01621459.1979.10482531Suche in Google Scholar

Diebold, F. and Mariano, R. (1991). Comparing predictive accuracy I: an asymptotic test. Discussion Paper 52, Institute for Empirical Macroeconomics, Federal Reserve Bank of Minneapolis, U.S.A.10.21034/dp.52Suche in Google Scholar

Diebold, F. and Mariano, R. (1995). Comparing predictive accuracy. J. Bus. Econ. Stat. 13: 253–263. https://doi.org/10.2307/1392185.Suche in Google Scholar

Ding, Y., Song, X., and Zen, Y. (2008). Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Syst. Appl. 34: 3081–3089. https://doi.org/10.1016/j.eswa.2007.06.037.Suche in Google Scholar

Dong, Y., Hamilton, R., and Tippett, M. (2014). Cost efficiency of the Chinese banking sector: a comparison of stochastic Frontier analysis and data envelopment analysis. Econ. Modell. 36: 298–308. https://doi.org/10.1016/j.econmod.2013.09.042.Suche in Google Scholar

Drake, M., Hall, M., and Simper, R. (2006). The impact of macroeconomic and regulatory factors on bank efficiency: a nonparametric analysis of Hong Kong’s banking system. J. Bank. Finance 30: 1443–1466. https://doi.org/10.1016/j.jbankfin.2005.03.022.Suche in Google Scholar

Färe, R., Grosskopf, S., and Lovell, K. (1985). The measurement of efficiency of production. KluwerNijhoff, Boston, p. 170.10.1007/978-94-015-7721-2Suche in Google Scholar

Gorton, B., and Metrick, A. (2010). Securitized banking and the run on repo. Yale ICF Working Paper No. 09-14, U.S.A.10.3386/w15223Suche in Google Scholar

Gunduz, Y. and Uhrig-Homburg, M. (2011). Predicting credit default swap prices with financial and pure data-driven approaches. Quant. Finance 11: 1709–1727. https://doi.org/10.1080/14697688.2010.531041.Suche in Google Scholar

Hansen, P. (2005). A test for superior predictive ability. J. Bus. Econ. Stat. 23: 365–380. https://doi.org/10.1198/073500105000000063.Suche in Google Scholar

Hansen, P., and Lunde, A. (2010). Mulcom 2.0, econometric toolkit for multiple comparisons. MIT University, Boston U.S.A.Suche in Google Scholar

Hansen, P., Lunde, A., and Nason, J. (2011). The model confidence set. Econometrica 79: 453–497. https://doi.org/10.3982/ECTA5771.Suche in Google Scholar

Hart, J. and Lee, C. (2005). Robustness of one-sided cross-validation to auto-correlation. J. Multivariate Anal. 92: 77–96. https://doi.org/10.1016/j.jmva.2003.08.005.Suche in Google Scholar

Hart, J. and Yi, S. (1998). One-sided cross-validation. J. Am. Stat. Assoc. 93: 620–630. https://doi.org/10.1080/01621459.1998.10473715.Suche in Google Scholar

Harvey, C. and Liu, Y. (2019). Cross-sectional alpha dispersion and performance evaluation. J. Financ. Econom. 134: 273–296. https://doi.org/10.1016/j.jfineco.2019.04.005.Suche in Google Scholar

Hoff, A. (2007). Second stage DEA: comparison of approaches for modelling the DEA score. Eur. J. Oper. Res. 181: 425–435. https://doi.org/10.1016/j.ejor.2006.05.019.Suche in Google Scholar

Holmstrom, B. and Tirole, J. (1997). Financial intermediation, loanable funds, and the real sector. Q. J. Econ. 112: 663–691. https://doi.org/10.1162/003355397555316.Suche in Google Scholar

Hwang, S. and Kao, T. (2008). Using two-stage DEA to measure managerial efficiency change of non-life insurance companies in Taiwan. Int. J. Manag. Decis. Making 9: 377–401. https://doi.org/10.1504/ijmdm.2008.019362.Suche in Google Scholar

Manahov, V., Hudson, R., and Gebka, B. (2014). Does high frequency trading affect technical analysis and market efficiency? And if so, how? J. Int. Financ. Mark. 28: 131–157. https://doi.org/10.1016/j.intfin.2013.11.002.Suche in Google Scholar

Mehran, H. and Thakor, A. (2011). Bank capital and value in the cross-section. Rev. Financ. Stud. 24: 1019–1067. https://doi.org/10.1093/rfs/hhq022.Suche in Google Scholar

Min, J. and Lee, Y. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst. Appl. 28: 603–614. https://doi.org/10.1016/j.eswa.2004.12.008.Suche in Google Scholar

Nayak, R., Mishra, D., and Rath, A. (2015). A Nave SVM-KNN based stock market trend reversal analysis for Indian benchmark indices. Appl. Soft Comput. 35: 670–680. https://doi.org/10.1016/j.asoc.2015.06.040.Suche in Google Scholar

Pasiouras, F., Tanna, S., and Zopounidis, C. (2009). The impact of banking regulations on banks’ cost and profit efficiency: cross country evidence. Int. Rev. Financ. Anal. 18: 294–302. https://doi.org/10.1016/j.irfa.2009.07.003.Suche in Google Scholar

Patel, J., Shah, S., Thakkar, P., and Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Syst. Appl. 42: 2162–2172. https://doi.org/10.1016/j.eswa.2014.10.031.Suche in Google Scholar

Pessarossi, P. and Weill, L. (2015). Do capital requirements affect cost efficiency? Evidence from China. J. Financ. Stabil. 19: 119–127. https://doi.org/10.1016/j.jfs.2014.11.002.Suche in Google Scholar

Racine, J. (2000). Consistent cross-validatory model-selection for dependent data: hv-block cross-validation. J. Econom. 99: 39–61. https://doi.org/10.1016/s0304-4076(00)00030-0.Suche in Google Scholar

Rosman, R., Wahab, N., and Zainol, Z. (2014). Efficiency of islamic banks during the financial crisis: an analysis of Middle Eastern and Asian countries. Pac. Basin Finance J. 28: 76–90. https://doi.org/10.1016/j.pacfin.2013.11.001.Suche in Google Scholar

Sakouvogui, K. (2019). Banks performance evaluation: a hybrid DEA-SVM- The case of U.S. agricultural banks. Accounting 5: 107–120.10.5267/j.ac.2018.9.002Suche in Google Scholar

Sakouvogui, K. (2020a). Impact of liquidity and solvency risk factors on the variance of cost efficiency measures. Manag. Finance 46: 883–895. https://doi.org/10.1108/mf-05-2019-0241.Suche in Google Scholar

Sakouvogui, K. (2020b). A comparative approach of stochastic frontier analysis and data envelopment analysis estimators: evidence from banking system. Journal of Economic Studies, https://doi.org/10.1108/JES-01-2019-0051 (Epub ahead of print).Suche in Google Scholar

Sakouvogui, K. and Shaik, S. (2020). Impact of financial liquidity and solvency on cost efficiency-evidence from US banking system. Studies in Economics and Finance, https://doi.org/10.1108/SEF-04-2019-0155 (Epub ahead of print).Suche in Google Scholar

Sakouvogui, K. and William, N. (2019). Robust CAPM estimation through cross validation. J. Financ. Data Sci. 1: 153–167. https://doi.org/10.3905/jfds.2019.1.2.153.Suche in Google Scholar

Sakouvogui, K., Shaik, S., and Addey, K. (2020). Cluster-adjusted DEA efficiency in the presence of heterogeneity: an application to banking sector. Open Econ. 3: 50–69. https://doi.org/10.1515/openec-2020-0004.Suche in Google Scholar

Sherman, D. and Gold, F. (1985). Bank branch operating efficiency: evaluation with data envelopment analysis. J. Bank. Finance 9: 297–316. https://doi.org/10.1016/0378-4266(85)90025-1.Suche in Google Scholar

Thagunna, S. and Poudel, S. (2013). Measuring bank performance of Nepali banks: a data envelopment analysis (DEA) perspective. Int. J. Econ. Financ. Issues 3: 54–65.Suche in Google Scholar

Vapnik, V. (1995). The nature of statistical learning theory. Springer-Verlag New York, Inc, U.S.A.10.1007/978-1-4757-2440-0Suche in Google Scholar

White, H. (2000). A reality check for data snooping. Econometrica 68: 1097–1126. https://doi.org/10.1111/1468-0262.00152.Suche in Google Scholar

Received: 2020-08-07

Accepted: 2020-12-04

Published Online: 2021-03-02

Published in Print: 2021-03-26

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Schlagwörter für diesen Artikel

DEA; economic efficiency measures; out-of-sample predictability; SVR; U.S. banks