Statistical Method of Goodness on Quantitative Models of Efficiency and Effectiveness

Abstract

Motivated by different qualitative constructs of efficiency and effectiveness [Cameron, 1978] and the variety of distinct quantitative models of measuring efficiency and effectiveness derived from them, we propose a statistical method of goodness on these quantitative models in the financial setting. Our statistical method of goodness is based on the semi- strong Efficient Market Hypothesis (EMH) [Ball and Kothari, 1994]. The semi-strong form of the EMH claims that stock prices reflect all publicly available information and that stock prices instantly change to reflect new public information. Fi- nancial markets can identify firms that are effective, “doing the right things” and efficient, “doing things right.” A firm that is “doing the right things right” is both efficient and effective and the market should value such firms higher than other firms. In our statistical model, we use market information and its derivatives such as stock price, market capital and TobinQ [Perfect and Wiles, 1994] as dependent variables. Efficiency and effectiveness measures are considered as independent variables. Our statistical method finds the best fit model from a family of functions and reports model parameters that are statistically significant. We apply our statistical method of goodness on two case studies of US and Indian bank data. In these case studies, we use existing models of efficiency and effectiveness [Kumar and Gulati, 2009] and explore other [Chu and Lim, 1998] quantitative models of profit maximization and cost minimization efficiency and effectiveness.