Essays on the Performance of US Bank Holding Companies

I employ nonparametric estimation techniques to explore different aspects of the performance of US bank holding companies (BHCs). In my paper \u27Market Pricing of U.S. Bank Holding Companies\u27 Technical Efficiency,\u27 I use quarterly data from 1986 to 2009 to examine whether changes in technical efficiencies of U.S. BHCs are reflected in their stock returns. The relationship between technical efficiency and stock returns is analyzed both in a financial and accounting framework. Efficiencies are estimated using the unconditional hyperbolic α-quantile estimator developed by Wheelock and Wilson (2008). This estimator is a local estimator and exhibits more desirable statistical properties than traditional estimators. For large bank holding companies, I find evidence of a weak link between technical efficiency and stock returns. I find no other persistent and robust relationship, an indication that the market may not value technical efficiency. In the paper \u27Restricting the Size of Banks May Have Costs,\u27 co-authored with Paul W. Wilson, returns to scale for BHCs are examined. The empirical evidence on scale economies among large BHCs operating in the U.S. is mixed, with some studies finding mild evidence of increasing returns to scale while most studies find no evidence of either increasing or decreasing returns. Most of studies have relied on estimation of fully parametric translog specifications of cost functions. We show that data on BHCs trivially reject the translog specification, and employ fully-nonparametric methods to estimate and make inference about returns to scale among U.S. BHCs. Our results suggest that both economically and statistically significant increasing returns to scale prevail throughout the range of sizes of BHCs. We use our estimates to provide rough estimates of the cost, in terms of foregone scale economies, of restricting the sizes of BHCs. The paper \u27Evolution of the U.S. Bank Holding Companies\u27 Performance over Time: Evidence from Nonparametric Efficiencies\u27 examines changes in the performance of U.S. BHCs between 1988 and 2010. The Malmquist index measures the total factor productivity change over time and can be decomposed into efficiency change and technology change. I use the nonparametric, unconditional, hyperbolic α-quantile estimator developed by Wheelock and Wilson (2008) to estimate three types of efficiencies: technical, cost, and revenue, that I then use to construct the decompositions of the Malmquist index. Results suggest that over the years, the largest banks experienced the largest gains, on average, in technical, cost and revenue efficiency, with the exception of 2005 -2010 period, and the smallest BHCs seem to have experienced gains in all efficiencies. Estimates of the technology change show a downward shift of the α-quantile (i.e., a decrease in the output produced for some given input used), an upward shift of the cost α-quantile (i.e., an increase in the minimum cost of producing some given output), and downward shift of the revenue α-quantile (i.e., a decrease in the amount of revenue generated from the output produced with some given amount of input), for most periods and class size, except the large BHCs