The relationship between banking market competition and risk-taking: Do size and capitalization matter?

This paper addresses the effects of bank competition on the risk-taking behaviors of banks in 10 Latin American countries between 2003 and 2008. We conduct our empirical approach in two steps. First, we estimate the Boone indicator, which is a measure of competition. We then regress this measure and other explanatory variables on the banking "stability inefficiency" derived simultaneously from the estimation of a stability stochastic frontier. Unlike previous findings, this paper concludes that competition affects risk-taking behavior in a non-linear way as both high and low competition levels enhance financial stability, while we find the opposite effect for average competition. In addition, bank size and capitalization are essential factors in explaining this relationship. On the one hand, the larger a bank is, the more it benefits from competition. On the other hand, a greater capital ratio is advantageous for banks that operate in collusive markets, while capitalization only enhances the stability of larger banks under high and average competition. These results are of extreme importance when considering bank regulations, especially in light of the recent turmoil in the global financial markets. (C) 2012 Elsevier B.V. All rights reserved.

A new long-term lifetime distribution induced by a latent complementary risk framework

In this paper, we proposed a new three-parameter long-term lifetime distribution induced by a latent complementary risk framework with decreasing, increasing and unimodal hazard function, the long-term complementary exponential geometric distribution. The new distribution arises from latent competing risk scenarios, where the lifetime associated scenario, with a particular risk, is not observable, rather we observe only the maximum lifetime value among all risks, and the presence of long-term survival. The properties of the proposed distribution are discussed, including its probability density function and explicit algebraic formulas for its reliability, hazard and quantile functions and order statistics. The parameter estimation is based on the usual maximum-likelihood approach. A simulation study assesses the performance of the estimation procedure. We compare the new distribution with its particular cases, as well as with the long-term Weibull distribution on three real data sets, observing its potential and competitiveness in comparison with some usual long-term lifetime distributions.

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

A behavioral intervention in a cohort of Japanese-Brazilians at high cardiometabolic risk

OBJECTIVE: To assess the effect of a health promotion program on cardiometabolic risk profile in Japanese-Brazilians. METHODS: A total of 466 subjects from a study on diabetes prevalence conducted in the city of Bauru, southeastern Brazil, in 2000 completed a 1-year intervention program (2005-2006) based on healthy diet counseling and physical activity. Changes in blood pressure and metabolic parameters in the 2005-2006 period were compared with annual changes in these same variables in the 2000-2005 period. RESULTS: During the intervention, there were greater annual reductions in mean (SD) waist circumference [-0.5(3.8) vs. 1.2(1.2) cm per year, p<0.001], systolic blood pressure [-4.6(17.9) vs. 1.8(4.3) mmHg per year, p<0.001], 2-hour plasma glucose [-1.2(2.1) vs. -0.2(0.6) mmol/L per year, p<0.001], LDL-cholesterol [-0.3(0.9) vs. -0.1(0.2) mmol/L per year, p<0.001] and Framingham coronary heart disease risk score [-0.25(3.03) vs. 0.11(0.66) per year, p=0.02] but not in triglycerides [0.2(1.6) vs. 0.1(0.42) mmol/L per year, p<0.001], and fasting insulin level [1.2(5.8) vs. -0.7(2.2) IU/mL per year, p<0.001] compared with the pre-intervention period. Significant reductions in the prevalence of impaired fasting glucose/impaired glucose tolerance and diabetes were seen during the intervention (from 58.4% to 35.4%, p<0.001; and from 30.1% to 21.7%, p= 0.004, respectively). CONCLUSIONS: A one-year community-based health promotion program brings cardiometabolic benefits in a high-risk population of Japanese-Brazilians.