Purpose of Study: The object of the study is the problem of financial management, in particular, the problem of forecasting the stage of developing bankruptcy of corporations-loaners and decision-making on the restructuring of credit debt. The subject of the research is the development of a dynamic model of bankruptcies with continuous time in conditions of high uncertainty and noise data, which allows diagnosing the stages of bankruptcy of the simulated object at any time (between the “time slices” in the data), as well as to predict the probability of bankruptcy in time ahead for a given horizon. Uncertainty is understood as a specific characteristic of the simulated class of dynamic bankruptcy problems –incompleteness and uncertainty in the data: in the training sample in the “time slices” only the boundary values of the probability of bankruptcy are specified (P=0 or P=1), i.e. there is no information about the intermediate values of P in the interval [0;1]. The uncertainty is determined by legal reasons: until the corporation is declared bankrupt by the arbitration court or the tax authorities, for it P=0, although objective accounting data may show proximity to bankruptcy. Methodology: The purpose of the study is to create an effective mathematical tool for predicting corporate bankruptcy to support decision-making on the financial management of corporations, which is focused on complex real-world modeling conditions. Results: On the basis of the system-wide law of inertia of the simulated dynamic system (or object), the original neuronet iterative logistic dynamic method (NLDM) is proposed, which allows eliminating the above incompleteness and uncertainty in the training sample and operate with continuous time in the procedures of diagnosis and prediction of bankruptcy stages of corporations-loaners. The adequacy of the dynamic model of bankruptcies is comprehensively investigated. The probability of correct identification of bankruptcies on the test set is not worse than 90%. The convergence of iterative procedures in the NLDM algorithm is investigated.

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АННОТАЦИЯ В статье исследуется проблема разработки информационно-математической модели для поддержки приня-тия решений по реструктуризации кредитной задолженности корпораций в банковских технологиях финан-сового менеджмента. Цель статьи -создание модели, позволяющей диагностировать стадии развивающегося кризиса корпораций в сложных условиях неполноты и зашумленности данных. Модель должна служить инструментом повышения объективности и качества принимаемых решений по реструктуризации кредитной задолженности корпораций. Исследование проводилось на основе нейросетевых методов моделирования и системного анализа, методов теории принятия решений, решения обратных задач интерпретации, т. е. извлечения новых знаний из данных. Разработан оригинальный метод построения нейросетевой логистической модели банкротств (НЛМБ) в слож-ных условиях моделирования. Новыми признаками метода, увеличивающими прогностическую силу модели, являются: 1) оптимальный отбор факторов с помощью байесовского ансамбля вспомогательных нейросетей, осуществляющих компрессию факторного пространства; 2) ступенчатая компрессия факторов на основе обоб-щенной функции желательности Харрингтона; 3) регуляризация основной (рабочей) нейросетевой модели на байесовском ансамбле нейросетей. НЛМБ апробирована на реальных данных корпораций строительной отрасли. Число верно идентифицированных объектов на тестовом множестве составило более 90% на всех нейросетях ансамбля. В НЛМБ достаточно высокое прогностическое качество нейросетевой модели обеспечивается новыми при-знаками метода и порождает эмерджентный эффект, проверенный в вычислительных экспериментах: улуч-шение качества нейросетевой модели по критерию правильно идентифицированных объектов Θ составляет 3,336 раза при компрессии факторов в 1,35 раза. НЛМБ может быть распространен на широкий круг задач финансового менеджмента. Ключевые слова: нейросетевая модель; стадии развития банкротства корпораций; поддержка решений ре-структуризации кредитной задолженности abStRaCt The article deals with the problem of developing an information and mathematical model to support decisionmaking on the restructuring of corporate debt in the banking technologies of financial management. The purpose of the article is to create a model that allows diagnostic of the stages of developing corporate crisis in difficult conditions of incomplete and noisy data. The model should serve as a tool for improving the objectivity and quality of decisions on the restructuring of corporate debt. The study was conducted on the basis of neural network modelling and system analysis methods, methods of decision-making theory, a solution of inverse problems of interpretation, i. e. extraction of new knowledge from data. We developed an original method of constructing neural network logistic model of bankruptcies (NNLMB) in the difficult conditions of the simulation. New features of the method, increasing the predictive power of the model, are: 1) optimal selection of factors using Bayesian ensemble of auxiliary neural networks, performing compression...

This article examines the factors influencing the change in stock prices of companies in the energy industry on the example of the largest company PJSC RusHydro, which are both subjective and objective. Subjective factors were grouped according to the greatest influence on the change in the studied indicators. The analysis of objective factors is based on the correlation between them and securities quotes. The research aims to predict the stock price three steps ahead using a trend model. As a result of the analysis, conclusions were drawn about the qualitative and quantitative relationship and dependence of indicators, as well as about the activities of the energy holding in the modern period.

The purpose of this article is the presentation of a novel and unconventional algorithm for bankruptcy risk management in banking technologies catered towards lending to legal entities (enterprises and companies). The challenges of assessing risk in this area primarily relate to the reduction of type I and type II errors when making decisions on the terms of lending (i.e. loan amounts and repayment parameters) on the ostensibly objective basis of a borrower’s creditworthiness assessment.   As such, it is necessary to use a unified procedure to select appropriate economic indicators for any bankruptcy model in order to reduce the high degree of uncertainty and noisiness of publicly available databases, and to take into consideration the specific character of knowledge-intensive, high-tech and “green” manufacturing. In order to approach this challenge, a mix of various methods is presented in this article, including credit scoring, neural simulation, a fuzzy model description, fuzzy inference rules, and a fuzzy Pospelov scale.   The research results are as follows: the authors have developed an unconventional algorithm for diagnosing corporate bankruptcy stages. This algorithm is based on the application of a system-wide law relating to decreases in integrated system entropy, contrasted with the sum of entropies of the relevant collated subsystems. This algorithm has been tested on a series of experimental observations of 30 agricultural enterprises in the Sterlitamak District of the Republic of Bashkortostan. We have thusly assessed the financial condition of borrowing companies, while controlling for the probability of a wide range of indicators.   Using this algorithm, the authors decided not to apply the rigorous requirements of the classical ‘least squares’ method used in regression analyses. A switch to a neural simulation approach in this algorithm necessitated an evaluation of the adequacy of the obtained model on the basis of a Bayesian approach. On the basis of this research, the authors propose that a regularisation of bankruptcy models has been achieved.