Study On The Normative Mechanism Model Of Corporate Finance Early Warning Monitor And Its Application Methods

In the last decade, with the development of globalized finance, the swell of credit market, and business administration innovations under e-commerce, risk management instruments for corporate finance management have been called for unprecedentedly. As a useful tool which could be able to predict business failure and set out early warning indicators to securities investors and managers, financial distress prediction models have been followed with wide interests and accepted as one of the foci in field of corporate finance. Because of inherent shortcomings, traditional mode-identify-styled financial early warning (FEW) prediction models depending on historical accounting data have difficultly been widely applied to corporate finance practice. However, FEW prediction models which could take both micro and macro factors into account have been in their library stage. It has been recognized that the key issues in the FEW modeling, such as the normative fundamental for choosing independent prediction variables, establishing effect manner of risk driving factors, normative modeling structure etc. have been not well solved at present, and the progressed research of these issues would be significant for both the engineering of corporate financial risk managerial instrument construction under e-commerce and the development of modern accounting and corporate finance theory. Through reviewing research paper in view of the characteristics of modern financial risk management engineering, it can be concluded that the progress of fundamental theory has lagged behind the improvement of modeling technique, which has resulted in that the limitations of modeling technique has been hardly broken through and scientific research achievements have hardly been applied in managerial practice as well. Therefore, FEW management methods modeling based on developing basal normative theory model, which could meet with the requirements of value chain risk management, is adopted as research subject, and the following work are organized in this dissertation.1. The self-adapted learning mechanism of FEW is constructed on a presented normative model of corporate flexible survival. On defining the connotations and denotations of corporate failure, the meaning of FEW is extended by the means of flexible financial decision making. Through comparing different explains of corporate failure in various economics theories, the viewpoints of economic evolution are absorbed, on which a normative flexible survival model is presented in manner of a series of theorems. Nine pivotal rules in flexible financial decision making are abstracted on analyzing the result of numerical model simulation. Whereafter, the learning mechanism in flexible financial decision structure is discovered and FEW principles centering self-adapted learning are constructed accordingly. It's concluded that to establish FEW system could not directlykeep corporate from financial distress and business failure, but could help corporate set up the formulas of learning circumstance changes initiatively to improve its elf-adapted adjustability. The validity of FEW management is laid by the capabilities of circumstance learning in decision making and self-adapted regulating in early warning tactics execution. Herewith, the limitation of both lacking normative theory support in current corporate failure prediction researches could be improved by the proposed self-adapted learning mechanism in FEW management.2. The normative hypotaxis relationships among systemic and non-systemic risk factors constructed in the above presented corporate flexible survival model are induced into KMV model to discuss the effects of risk driving factors' fluctuations on corporate default risk migration, by which the default risk early warning monitor method with multi-driving-factor's and continually financing. Based on a proposed continual profit function with expected return rate and mixed time structure, equity and debt evaluation models are presented with taking both multi credit risk driving factors and continually financing into account. Then the structural normative relationships among multi driving factors and credit risk are systematically built by means of KMV's solving expected default risk, on which default risk drifting shocked by twelve risk driving factors' fluctuations are simulated numerically. By using a square non-linear early warning regions dividing method in analyzing results of numerical simulation, pivotal default risk early warning range and their monitoring principles in driving factors' fluctuation observation are achieved, in which mulriple partialities in creditor's risk evaluation are revealed. The above discussion accomplishes parameters analysis of the fonner proposed FEW methods, and the manner of directly taking the effects of risk driving factors on default risk into the structure of credit risk evaluation modeling have been widen as well.3. The characteristics of value flow under integrated ERP cooperating with organizational structure evolution of supply chain are reached, on which the interior and exterior structure of FEW system are designed. For the purpose of improving decision-making relevance, the value modules dividing methods are proposed on the presented value flow integrating model under the condition that the lacked relationships among various value-based management methods are remedied, on which the managerial parts that the value module FEW should act on and their functional objective are therefore obtained. It is proved that three proposed parameters of allocating capital in the value flow integrating model, default critical lever, lever compensation with expected yield and asset liquidity distribution, could provide solutions for the problem that decision making un-relevance results in un-synchrony value flow on value chain. Moreover, in view of the relationships among various value modules, to realize self-adapted leaning the effects of supply chain risk factors on value chain performance should be the kernel of value chain's FEW modeling.4. Different supply chain risk factors FEW models for divided value modules are developed respectively: (1) The activity value flexibility control model is developed for activity module, on which activity technique selection tactics restricted by the value flow characteristics could be obtained. By means of a decision information extracting design on integrated ERP, the rational technique selection ranges could be timely got. The numerical result of experimentation shows that rational execution technique selection could support activity outsourcing decision making to respond the changing activity chain circumference and custom value. (2) The value performance FEW model is presented for the activity chain module by means of establishing linear transform relationship among warning indicator degree matrix, supply chain risk factors monitor matrixes, FEW decision authorization matrixes and risk tackling tactics matrixes. The mechanism of the model cooperating with budget control system could solve the problems of probably invalidities of budget rigid control invalidity in changing circumstance. The results of model numerical experimentation demonstrates that negative effects of supply chain risk on activity chain value could be counteracted by the rising liquidity risk, and therefore expected return could be achieved. (3)The default lever margin monitor model is proposed for supply chain expected going-on value management. First, the effects of profit tax lever and tax shelter of debt interest on supply chain default risk are analyzed under determined demand increase. Then, with the condition of uncertain demand increase, default lever margin is solved on taking both learning capacity in decision making and investment risk partiality into account. The numerical results demonstrate that default lever margin could provide early warning indicators for supply chain default risk. Through designing the cooperating working process among the above FEW models, it is found that the above value performance FEW models could construct the self-adapted learning mechanism to supply chain risk factors. These models could be in effect independently, and could also be referential for establishing value chain early warning decision mechanism.