Scoring Modeling in Estimating the Financial Condition of Russian Agro-Industrial Companies

Scoring Modeling in Estimating the Financial Condition of Russian Agro-Industrial Companies

Oleg Y. Patlasov (Omsk Regional Institute, Russia) and Olga K. Mzhelskaya (Omsk Humanitarian Academy, Russia)
Copyright: © 2019 |Pages: 25
DOI: 10.4018/978-1-5225-7760-7.ch008
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The chapter presents the authors' estimations according to the scoring modeling techniques; also, internationally spread models of bankruptcy forecasting are systematized. Advantages and disadvantages of dynamic modelling methods as applied to financial condition assessment are presented here. Methodological problems of financial modelling are explained here in detail. Regression, logit-regression, and discriminant models are built on the basis of data on the Rosselkhozbank and Sberbank of Russia regulations, taking into account the agrarian specifics of organizations and regional specificity of the Omsk region. An attempt has been made to balance the simplicity of calculations and the accuracy of predictions. Graphs, to be used for express analysis, are constructed on the basis of two core financial indicators.
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Nowadays managers’ tasks include not just optimal combination of production factors, based on the knowledge of production technology and own organizational capabilities, but also on taking into account the current marketing situation, financial analysis and then making the right managerial decisions. Success of entrepreneurship is thus predetermined by natural and technological factors, and also organizational and sociopolitical factors and finally, by the variety of business risks. Contemporary management requires the knowledge of bankruptcy diagnostics, business financial position assessment, its operational prospects, possible actions of creditors, internal opportunities for debt restructuring etc.

Depending on the economic analysis objectives, attention can be focused on the following aspect: company’s liquidity and debt service indicators, important (for creditors); profitability and equity in their dynamics (for shareholders); labor costs, volume and efficiency of capital expenditures (for labor analysts); accuracy in calculations and payment of taxes (for public authorities’ representatives). Arbitration managers should become convinced in the absence of premeditated and fictitious bankruptcy, and for this they should assess company’s real net assets, the extent of overdue debts etc., the execution of the already announced court decisions. From the standpoint of managers, financial analysis and managerial accounting are management function; and from the standpoint of competitors’ comparative analysis, economic analysis is part of benchmarking.

In the framework of economic analysis, the following forms of comparative analysis have become the most common:

  • 1.

    Comparative analysis of financial ratios of the given economic subject with the average industry indicators.

  • 2.

    Comparative analysis of financial indicators with the related data from competing enterprises.

  • 3.

    Comparative analysis of financial indicators of separate structural units and separate divisions of a legal entity (its responsibility centers).

  • 4.

    Monitoring of reporting according to the planned and/or regulatory financial indicators.

It is always easier to assess the merits and bottlenecks of any official analysis method in scientific terms, however, in practical terms, it is always not subject to correction because all corrections immediately become the prerogative of the official body that has approved and imposed this method. Complex methods of analysis are indisputably more labor-consuming, but they are necessary for organizations themselves, namely, for their more grounded managerial decision-making, whereas for external users (tax revenue offices, creditors, banks etc.) it is necessary to conduct more punctual analysis according to the already approved and well established methods.

The currently used methods of the organizations’ financial condition analysis all have a serious drawback: their conclusions are often based only on the accounting reports’ data, thus, they do not take into consideration the current stage of a company’s life cycle and/or its potential future situation. For proper managerial decisions, it is advisable to conduct system analysis which includes, in addition to assessing the financial state of an organization, the current state of the external environment and the human factor too. The maximum effect from the conducted diagnostics is achieved when this diagnostics is of complex character, however, such procedures are, of course, much more challenging since they are time- and cost-demanding.


Western Bankruptcy Diagnostics Toolkit

Different methods of predicting bankruptcy forecast various types of crises and, accordingly, estimations obtained with their help are also very different. The choice of specific methods is prescribed and adjusted in accordance with the specifics of a particular industry/branch in which the organization operates. As a result, a large number of different models for bankruptcy forecasting have been developed.

Financial analysis models differ depending on the research principles and analysis priorities. The key of them are as follows:

Key Terms in this Chapter

Wilks’ Lambda: A measure of how well each function separates cases into groups. It is equal to the proportion of the total variance in the discriminant scores not explained by differences among groups. Smaller value of Wilks’ lambda indicates greater discriminatory ability of the function.

Tolerance: A variable defined as 1 minus the squared multiple correlation of this variable with all the other independent variables. Therefore, the smaller is the tolerance of a variable, the more redundant is its contribution to the regression (i.e., it is redundant with the contribution of other independent variables). If the tolerance of any of the variables in the regression equation is equal to zero (or is very close to zero), then the regression equation cannot be evaluated (the matrix is said to be ill-conditioned, and it cannot be inverted).

Structure Coefficients: Structure coefficients add to the information provided by ß weights. Betas inform us as to the credit given to a predictor in the regression equation, while structure coefficients inform us as to the bivariate relationship between a predictor and the effect observed without the influence of other predictors in the model. If the coefficient is closer to zero, the relationship between the individual variable and the discriminant function is insignificant.

Mahalanobis Distance: A measure of how much the particular case values of the independent variables differ from the average of all cases. Larger Mahalanobis distance identifies the case with the extreme values on one or more of the independent variables.

Raw Coefficients: Coefficients providing information about the absolute contribution of a variable to the value of the discriminant function.

Discriminant Score: The score of each respondent on the discriminate function in discriminant analysis.

Discriminant Function: A function of several variables used to assign items into one of two or more groups. The function for a particular set of items is obtained from measurements of the variables belonging to a known group.

Solvency: Is the ability of a company to meet its long-term financial obligations. Solvency is essential to staying in business as it asserts company’s ability to continue operations into the foreseeable future. Liquidity should not be confused with solvency, however. Solvency is directly related to the ability of an individual or business to pay their long-term debts including any associated interest. To be considered solvent, the value of assets, company’s or individual, must be greater than the sum of debt obligations.

Discriminant Variables: Characteristics used to distinguish one class from another; they should be measured either on the interval scale, or on the scale of relations. Thus, it becomes possible to calculate the mathematical expectations, variances, etc.

Discriminant Weights: Standardized discriminant weight (discriminant coefficient) of a certain sign and weight is assigned to each variable in computing of discriminant functions. When the sigh is ignored, each weight represents the relative contribution of its associated variable to that function, while independent variables with relatively larger weights contribute more to the discriminant power of the function than do variables with smaller weights.

F-Statistics: Consists of F-enter and F-remove: 1) The use of F-value. A variable is entered into the model if its F-value is greater than the Entry value and is removed if the F-value is less than the Remove value. Entry must be greater than the removal one, and both values must be positive. To enter more variables into the model, its author is supposed to lower the Entry value. To remove more variables from the model, the Removal value must be increased. 2) The use of probability with F. A variable is entered into the model if the significance level of its F-value is less than the Entry value and is removed if the significance level is greater than the Removal value. Entry must be less than Removal, and both values must be positive. To enter more variables into the model, one should increase the Entry value. To remove more variables from the model, the Removal value must be lowered. At each step, the predictor with the largest F-enter, the value of which exceeds the entry criteria, is added to the model.

Standardized Coefficients: Standardized coefficients refer to how many standard deviations a dependent variable will change per standard deviation increase in the predictor variable.

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