2 resultados para Bankruptcy, conglomeration, mergers, spin-offs, project finance
em Digital Commons at Florida International University
Resumo:
The single spin asymmetry, ALT ′, and the polarized structure function, σ LT′, for the p( e&ar; , e′K +)Λ reaction in the resonance region have been measured and extracted using the CEBAF Large Acceptance Spectrometer (CLAS) at Jefferson Lab. Data were taken at an electron beam energy of 2.567 GeV. The large acceptance of CLAS allows for full azimuthal angle coverage over a large range of center-of-mass scattering angles. Results were obtained that span a range in Q 2 from 0.5 to 1.3 GeV2 and W from threshold up to 2.1 GeV and were compared to existing theoretical calculations. The polarized structure function is sensitive to the interferences between various resonant amplitudes, as well as to resonant and non-resonant amplitudes. This measurement is essential for understanding the structure of nucleons and searching for previously undetected nucleon excited states (resonances) predicted by quark models. The W dependence of the σ LT′ in the kinematic regions dominated by s and u channel exchange (cos qcmk = −0.50, −0.167, 0.167) indicated possible resonance structures not predicted by theoretical calculations. The σLT ′ behavior around W = 1.875 GeV could be the signature of a resonance predicted by the quark models and possibly seen in photoproduction. In the very forward angles where the reaction is dominated by the t-channel, the average σLT ′ was zero. There was no indication of the interference between resonances or resonant and non-resonant amplitudes. This might be indicating the dominance of a single t-channel exchange. Study of the sensitivity of the fifth structure function data to the resonance around 1900 MeV showed that these data were highly sensitive to the various assumptions of the models for the quantum number of this resonance. This project was part of a larger CLAS program to measure cross sections and polarization observables for kaon electroproduction in the nucleon resonance region. ^
Resumo:
Bankruptcy prediction has been a fruitful area of research. Univariate analysis and discriminant analysis were the first methodologies used. While they perform relatively well at correctly classifying bankrupt and nonbankrupt firms, their predictive ability has come into question over time. Univariate analysis lacks the big picture that financial distress entails. Multivariate discriminant analysis requires stringent assumptions that are violated when dealing with accounting ratios and market variables. This has led to the use of more complex models such as neural networks. While the accuracy of the predictions has improved with the use of more technical models, there is still an important point missing. Accounting ratios are the usual discriminating variables used in bankruptcy prediction. However, accounting ratios are backward-looking variables. At best, they are a current snapshot of the firm. Market variables are forward-looking variables. They are determined by discounting future outcomes. Microstructure variables, such as the bid-ask spread, also contain important information. Insiders are privy to more information that the retail investor, so if any financial distress is looming, the insiders should know before the general public. Therefore, any model in bankruptcy prediction should include market and microstructure variables. That is the focus of this dissertation. The traditional models and the newer, more technical models were tested and compared to the previous literature by employing accounting ratios, market variables, and microstructure variables. Our findings suggest that the more technical models are preferable, and that a mix of accounting and market variables are best at correctly classifying and predicting bankrupt firms. Multi-layer perceptron appears to be the most accurate model following the results. The set of best discriminating variables includes price, standard deviation of price, the bid-ask spread, net income to sale, working capital to total assets, and current liabilities to total assets.