3,120 research outputs found
Failure prediction of Chinese A-share listed companies : comparisons using logistic regression model and neural network analysis : a thesis presented in partial fulfilment of the requirements for the degree of Master of Business Studies in Finance at Massey University, Palmerston North, New Zealand
This study compares the relative prediction accuracy of corporate failure between two prediction methods –logistic regression model and neural network analysis– based on a sample of 3598 observations and companies data obtained from the Chinese A- Share market during the period 1991 to 2002. Seven criteria have been set up to define failure according to attributes of Chinese listed companies. Using forty financial ratios and seven misclassification cost ratios of Type I and Type II error, two models achieve ranges of minimal misclassification cost at optimal cut-off points for two years prior to business failure; The logistic regression model is slightly superior to neural network analysis. Compared with random prediction, both models are efficient. In addition, the study points out that Total Asset Turnover (TATR), Cash Ratio (CASR), Earning per Share (EPS), Total Debt to Total Asset (TDTA), Return on Assets (ROA) and the natual log of Total Market Value (MVLN) could be significant financial indictors of corporate failure. Results of the study have important implications in credit evaluation, internal risk control and capital market investment guidelines
Power spectrum of the fluctuation of Chebyshev's prime counting function
The one-sided power spectrum of the fluctuation of Chebyshev's weighted prime
counting function is numerically estimated based on samples of the fluctuating
function of different sizes. The power spectrum is also estimated analytically
for large frequency based on Riemann hypothesis and the exact formula for the
fluctuating function in terms of all the non-trivial Riemann zeroes. Our
analytical estimate is consistent with our numerical estimate of a 1/f^2 power
spectrum
Modelling the Self-similarity in Complex Networks Based on Coulomb's Law
Recently, self-similarity of complex networks have attracted much attention.
Fractal dimension of complex network is an open issue. Hub repulsion plays an
important role in fractal topologies. This paper models the repulsion among the
nodes in the complex networks in calculation of the fractal dimension of the
networks. The Coulomb's law is adopted to represent the repulse between two
nodes of the network quantitatively. A new method to calculate the fractal
dimension of complex networks is proposed. The Sierpinski triangle network and
some real complex networks are investigated. The results are illustrated to
show that the new model of self-similarity of complex networks is reasonable
and efficient.Comment: 25 pages, 11 figure
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