RANK CORRELATION METHODS FOR BUSINESS ENTITY PERFORMANCE

Abstract

The development of modern society necessitates the study of complex economic systems through the application of systematic approaches and related methods. One of the main challenges in conducting such research lies in the mass nature of most economic processes. Another challenge arises from the dynamic nature of economic systems. The functioning of a business entity as a complex dynamic system requires the use of generalized indicators in the decision-making process, among which the market value of the business entity is of particular importance. Today, value-based management is considered the core parameter reflecting the economic potential of a business entity, while the dynamics of changes in its market value serve as a key indicator of the quality of management. Practically the only structured and logically justified method of studying economic systems is their modeling. A central role in this context is played by mathematical models, whose toolkit includes various mathematical instruments. Among them, correlation-regression analysis methods and time series analysis methods are particularly emphasized. Since determining the market value of a business entity relies on forecasted values of net assets and net financial results, the use of time series methods — such as the method of analytical alignment or the moving average method — is deemed appropriate. To obtain a more accurate estimate of market value, it is advisable to first examine the presence and strength of the relationship between the mentioned factors. In this context, non-parametric methods of analyzing relationships are considered especially useful. These include measures of association such as the Chuprov coefficient, Cramér’s V, Pearson’s coefficient, contingency coefficient, association coefficient, and odds ratio; as well as rank correlation coefficients such as Kendall’s tau and Spearman’s rho, and Fechner’s coefficient.