Trying To understand all-equity firms

This paper studies all equity firms and shows which are in US firms, the main drivers of zero-debt policy. I analyze 6763 U.S. listed companies in years 1987-2009, a total of 77442 firms year. I find that financial constrained firms show a higher probability to become unlevered. In the opposite side, firms producing high cash flow are also likely to become unlevered, paying their debt. Some firms create economies of scale in the use of funds, increasing the probability of become unlevered. The industry characteristics are also important to explain the zero-debt policy. However is the high perception of risk, the most important factor influencing this extreme behavior, which is consistent with trade-off theory.

Indicadores prognósticos na hipertensão pulmonar

Mestrado em Tecnologia de Diagnóstico e Intervenção Cardiovascular - Ramo de especialização: Intervenção Cardiovascular

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.