Fixed and floating charges : the latest conflicting law and how to address the gaps

Can book debts be subject to a fixed charge? This question was considered by the House of Lords in National Westminster Bank v. Spectrum Plus Limited [2005] UKHL 41 where the full House was against the idea of a fixed charge on book debts and insisted that only a floating charge had been created. The law in this area is still vague and uncertain in Australia. This paper argues that the financiers and the companies should be given the freedom to decide how they wish to structure their charge documents. The paper sets out to argue that, in respect to the use of book debts as security for a loan, the only way for both the financiers and the companies to do business is to create a sustained workable fixed charge or even multiple fixed and floating charge on book debts. The author explains how this could be possible and how the proposed model would not deny the statutory priority rights of the preferential creditors.

Managing knowledge and intangible assets

This comprehensive handbook is very popular with HR practitioners, line managers, and anyone else who needs an overview of the legal and managerial aspects of managing people in organisations.

In this edition, over 50 chapters have been updated to reflect current workplace relations law, including the new Forward with Fairness Transition Act changes. There is also a new chapter on "Government Funded Traineeships - A guide for HR Professionals".

Definition and quantification of assets

The word ‘asset’ was originally taken into the English language, from the Latin ‘ad satis’ and French ‘asez’, as a term used at law meaning sufficient estate or effects to discharge debts. It later came to be used in the sense of property available for the payment of debts. Assets were understood to be property (objects owned and rights of ownership) that could be exchanged for cash. The importance of factual knowledge of the money equivalents of property and debts, in managing mercantile affairs, was emphasised in accounting manuals during the eighteenth and nineteenth centuries. The rights of investors and creditors to factual up-to-date information about the financial state of affairs of companies, given the advent of limited liability, underscored the early company legislation that required the preparation and auditing of statements of property and debts. During the latter part of the nineteenth century the emphasis in accounting moved away from assets as exchangeable property to assets as deferred costs. Expectations took the place of observables. The abstract (expectational) notion of assets as ‘future economic benefits’ was embraced by accountants in the absence of rigorous definitions of the elements and functions of dated statements of financial position and performance. Assets are quantified financially by a heterogeneous mass of potentially inconsistent rules that, by and large, have no regard for the empirical nature of measurement. Consequently, accountants have failed to provide the community with up-to-date factual information about the financial state of affairs and performance of business entities - and, hence, with an informative basis for financial action.

Sequential fixed-width confidence bands for kernel regression estimation

We consider a random design model based on independent and identically distributed (iid) pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths.

The sample size was optimized using the purely and two-stage sequential procedure together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confidence bands based on the local linear estimator have the best performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.

Sample size determination for kernel regression estimation using sequential fixed-width confidence bands

We consider a random design model based on independent and identically distributed pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths. The sample size was optimized using the purely and two-stage sequential procedures together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confi dence bands based on the local linear estimator have the better performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.

A novel monotonic fixed-point algorithm for l1-regularized least square vector and matrix problem

Least square problem with l₁ regularization has been proposed as a promising method for sparse signal reconstruction (e.g., basis pursuit de-noising and compressed sensing) and feature selection (e.g., the Lasso algorithm) in signal processing, statistics, and related fields. These problems can be cast as l₁-regularized least-square program (LSP). In this paper, we propose a novel monotonic fixed point method to solve large-scale l₁-regularized LSP. And we also prove the stability and convergence of the proposed method. Furthermore we generalize this method to least square matrix problem and apply it in nonnegative matrix factorization (NMF). The method is illustrated on sparse signal reconstruction, partner recognition and blind source separation problems, and the method tends to convergent faster and sparser than other l₁-regularized algorithms.