A PREDICTIVE APPROACH TO THE TAXONOMIC ANALYSIS OF A COMPANY'S FINANCIAL AND ECONOMIC HEALTH

Анотація

Objective. The purpose of the article is to identify and evaluate alternatives to the application of the predictive approach to the taxonomic analysis of the financial and economic state of the enterprise.. Methods. During the research, the following methods are applied: the method taxonomy of (for taxonomic analysis based on the data of the enterprise «Kyivstar» PJSC); the method of analysis (for comparing integral and complex taxonomic indicators for different periods; the comparison method (for comparing the accuracy of the obtained forecast models); the autoregression forecasting method (for forecasting the input indicators for taxonomic analysis of «Kyivstar» PJSC and the taxonomic indicators themselves). Results. The expediency of applying the predictive approach to the taxonomic analysis of the financial and economic state of the enterprise on the example of PJSC "Kyivstar" is substantiated. It was determined that the application of the predictive approach allows not only to assess the current state of the enterprise, but also to identify trends and predict the future development of enterprises, contributes to more justified management decisions, increasing the effectiveness of strategic planning. Two directions of application of the predictive approach to the taxonomic analysis of the financial and economic state of the enterprise are proposed: forecasting of taxonomic indicators, forecasting of input data and calculation of taxonomic indicators based on actual and model data. It was established that the accuracy of forecasting according to the first approach (forecasting of taxonomic indicators) for integral indicators characterizing the company's income (I1), company's expenses (I2), the efficiency of the company's non-current assets (I3), the efficiency of the use of the company's own capital (I4), profitability and financial stability of the enterprise (I5), and the complex taxonomic indicator was 73%-86%, according to the second approach (forecasting input data and calculating taxonomic indicators based on actual and model data) - 78-91%. It was determined that the advantage of the first approach (forecasting taxonomic indicators) is a simple calculation algorithm, while forecasting is applied to only six resulting taxonomic indicators; the second (prediction of input data and calculation of taxonomic indicators based on actual and model data) – a more reasonable conclusion regarding the accuracy of the predicted values ​​of taxonomic integral and complex indicators.

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