Out-of-Sample Predictability of the Cost Efficiency Measures of US Banks: Evidence of Capital Requirements

In the evaluation of capital adequacy requirements on the economic efficiency measures of banks, the literature has presented a two-stage Data Envelopment Analysis approach. In the first stage, the efficiency measures are estimated. In the second stage, the relationship between capital adequacy requirements and efficiency measures treated as the dependent variable is evaluated using the classical ordinary least squares (OLS) model. However, determining the out-of-sample predictability of capital adequacy requirements has been lacking. Therefore, this paper contributes to the sparse literature of capital adequacy requirements by applying Support Vector Regressions (SVRs) and the routinely used OLS linear model benchmarks. Using a total of 10,380 December quarterly observations of United States’ commercial and domestic banks from 2008 to 2019, we estimate the model parameters of SVRs with Linear, Polynomial and Radial basis function kernels and motivated by h-block crossvalidation technique. The results reveal that the SVRs provides better benchmarking insights in the evaluation of financial banks’ capital adequacy requirements than the benchmark OLS model.