Operationalization of the theory of forecasting default: a conceptual model
- Authors: Makarov I.M.1
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Affiliations:
- Issue: No 4 (2024)
- Pages: 66-86
- Section: ARTICLES
- URL: https://journals.rcsi.science/2409-8647/article/view/360642
- EDN: https://elibrary.ru/SBWDAO
- ID: 360642
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Abstract
The subject of the study is models that assess the risk of a company's default and, conversely, its financial health. The article forms a clear conceptual understanding of the phenomenon of "default", which causes financial difficulties for the company: how they begin, develop and escalate to analyze and predict the borrower's future poor performance and assess the possibility (risk) of his inability to meet his obligations on time. The main objective of the study is to develop a model for quantifying the probability of default within a consistent probabilistic framework (Bayes model), where the factors of idiosyncratic risk — assessed using soft information and human skills — are fundamental to understanding. The financial health of a company depends on maintaining a balance between its demand for credit and supply in the credit market. The main contribution of this research is to develop a theory of the financial health of a company based on maintaining equilibrium in financial systems characterized by the long-term effect of manipulating expectations in dynamic agency settings with training and uncertainty, as well as with interdependent remuneration systems of principals and agents. Within the framework of an agreed probabilistic structure — the Bayesian interpretation — the second contribution is the development of a model capable of calculating the probability of default and setting ranges of equilibrium interest rates, within which the contractual powers and competitive forces of operators find common ground depending on the predictable performance of the company (variability of cash flow factors), changes in its financial structure (leverage intensity, the structure of debt repayment periods) and predictable trends in credit supply conditions (rate curves, competition, availability of information, analytical tools, etc.) Credit risk measurement tools and the operators who use them must take a step back in order to move forward, mastering the technical aspects of fundamental analysis over and over again.
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