Technical Efficiency Through Innovative Methods and Estimations in Financial Markets

Introduction

In stochastic frontier model analysis, it is acknowledged that the estimation of production functions must respect the fact that actual production cannot exceed maximum possible production given input quantities. Consequently, one of the main questions is to investigate the relationship between inefficiency and a number of factors which are likely to be determinants and measure the extent to which they contribute to the presence of inefficiency. Overall, these determining factors characterize the process of technological change. Stochastic frontier models assume that producers operate under the same production technology and that the inefficiency distribution across individuals and time are homogeneous. Estimation of technical efficiency has been the subject of research in many empirical studies on industrial productivity, contributing to the theoretical development and empirical application of SFA at both the firm and industry levels, with the purpose of screening out the external effects and statistical noise from the producer’s performance and achieving a more accurate efficiency measure (Wang, 2000). Following these fundamental approaches, there has been a rapid increase in the volume of research on analysis of efficiency in production, both in theoretical and empirical research. Most of the literature focused mainly on stochastic frontier model with distributional assumptions by which efficiency effects can be separated from stochastic element in the model and for this reason a distributional assumption has to be made. Unobservable individual effects also play an important role in the estimation of panel stochastic frontier models. In contrast to the conventional panel data literature, however, studies using stochastic frontier models often interpret individual effects as inefficiency (Schmidt and Sickles, 1984), such as technical inefficiency in a stochastic production frontier model.

This chapter focuses on reviewing the stochastic frontier analysis literature regarding estimating inefficiency in financial markets level, as well as explaining producer heterogeneity along with the relationships with productive efficiency level. The chapter begins with a general overview of the main research papers on estimating productive efficiency in financial markets, both in aggregate and disaggregate level, providing the main hypotheses and results of each case. Then, the chapter continues with explaining producer heterogeneity, as well as the main determining factors towards efficiency differentiations.

A question of interest is whether inefficiency occurs randomly across producers, or whether some producers have predictably higher levels of inefficiency than others. If the occurrence of inefficiency is not totally random, then it should be possible to identify factors that contribute to the existence of inefficiency (Reifschneider and Stevenson, 1991). The important task is to relate inefficiency to a number of factors that are likely to be determinants and measure the extent to which they contribute to the presence of inefficiency. Following these foundamental approaches, there has been a rapid increase in the volume of research on analysis of efficiency in production, both in theoretical and empirical research (Cowan and Salotti, 2015 and Iftekhar and Soula, 2017).