An Empirical Research Survey of the Productivity and Efficiency of the Securities Industry

This paper provides a review of the approaches that have been used to evaluate the productivity and efficiency of the securities industry (financial ratios, econometric techniques, data envelopment analysis-DEA, and the estimation of stochastic frontiers). In undertaking this it identifies some of the organisational structure characteristics of securities companies that are associated with high levels of operational efficiency, particularly regarding the impact of regulation, the structure of ownership, and economies of scope and scale. The findings are that from the limited number of studies that have to date been undertaken it appears that the industry does have economies of scale, benefits from deregulation and performs better with foreign and bank ownership.

sfcount: Command for count-data stochastic frontiers and underreported and overreported counts

In this article, we introduce a new command, sfcount, to fit count-data stochastic frontier models. Although originally designed to estimate production and production-cost functions, this new command can be used to estimate mean regression functions when count data are suspected to be underreported or over-reported.

Stochastic frontiers of efficiency for Brazilian investment funds: a panel data analysis

Foundations, methodological and empirical possibilities of measurement and analysis in the performance of financial investments within investment funds have been developed since they were once introduced in the 1970s, thus establishing a path of growing acceptance in financial markets and universities’ academies. The first approaches over the efficiency of these funds, considering their stochastic implications, occurred in the late 1990s and have evolved with the help of SFA – Stochastic Frontier Analysis, although it still needs more careful verification. This article measured and analyzed the stochastic frontier of efficiency over 33 different Brazilian investment funds from 2012 to 2015. For doing so, Battese and Coelli’s (1995) specifications was used. It shows the effects of inefficiencies, which are defined as explicit functions of specific factors in the context of panel data funds. They are estimated by the maximum likelihood method. Sharpe ratios (SR) were also calculated for comparative purposes. Based on these two indicators (SFA and SR), the most recommendable funds to invest and the ones in which the application should not be performed were identified. Such procedures have stimulated the necessary and promising studies, as well as future researches, which, in turn, may establish new methodological formulation as an efficient and effective instrument to choose the best and the safest funds to invest.