DETERMINATION OF THE PLACE OF FINANCIAL TOP - STRENGTH BASED ON THE TOTAL VALUE OF NON-FINANCIAL COMPANIES

The article develops a rating methodology for assessing the quality of the balance sheet of non-financial companies on the basis of the generalized conditional reference balance. Depending on the quality of the balance of non-financial companies, 4 gradations of quality were established for their classification. In addition, in order to find out the leading positions of non-financial companies, the financial size of non-financial companies was introduced, which is a new economic term, and a methodology for calculating it was given. Depending on the financial size of non-financial companies, the financial size was divided into 9 stages to determine their comparable total cost

Desarrollo e investigación de modelos de puntuación de colecciones basados en la plataforma analítica Deductor

Este artículo resuelve el problema de la construcción e investigación de modelos de puntuación de colecciones. Se destaca la relevancia de resolver este problema sobre la base de las tecnologías de modelado inteligente: árboles de decisión, regresión logística y redes neuronales. Los datos iniciales de los modelos fueron un conjunto de 14 columnas y 5779 filas. La construcción de los modelos se realizó en plataforma Deductor. Cada modelo fue probado en el conjunto de 462 registros. Para todos los modelos se construyó la correspondiente matriz de clasificación y se calcularon los errores de 1º y 2º tipo, así como el error general de los modelos. En términos de minimizar estos errores, la regresión logística mostró los peores resultados y la red neuronal mostró los mejores. Además, se evaluó la efectividad de los modelos construidos según criterios de «ingresos» y «tiempo». Por el tiempo que cuesta el modelo de regresión logística supera a otros modelos. Sin embargo, en términos de ingresos, el modelo de red neuronal fue el mejor. Así, los resultados mostraron que para minimizar el tiempo dedicado al trabajo con los deudores es recomendable utilizar un modelo logístico. Sin embargo, para maximizar las ganancias y minimizar los errores de clasificación, es apropiado utilizar un modelo de red neuronal. Esto indica su eficacia y posibilidad de uso práctico en sistemas de puntuación inteligentes

Desarrollo e investigación de modelos de puntuación de colecciones basados en la plataforma analítica Deductor

This article solves the problem of collection scoring models constructing and researching. The relevance of solving this problem on the intelligent modeling technologies basis: decision trees, logistic regression and neural networks is noted. The initial data for the models was a set of 14 columns and 5779 rows. The models construction was performed in Deductor platform. Each model was tested on the set of 462 records. For all models, the corresponding classification matrix were constructed and the1st and 2nd kind errors were calculated, as well as the general error of the models. In terms of minimizing these errors, logistic regression showed the worst results, and the neural network showed the best. In addition, the constructed models effectiveness was evaluated according to «income» and «time» criteria. By the time costs the logistic regression model exceeds other models. However, in terms of income the neural network model was the best. Thus, the results showed that in order to minimize the time spent on work with debtors it is advisable to use a logistic model. However, to maximize profits and minimize classification errors, it is appropriate to use a neural network model. This indicates its effectiveness and practical use possibility in intelligent scoring systems.Este artículo resuelve el problema de la construcción e investigación de modelos de puntuación de colecciones. Se destaca la relevancia de resolver este problema sobre la base de las tecnologías de modelado inteligente: árboles de decisión, regresión logística y redes neuronales. Los datos iniciales de los modelos fueron un conjunto de 14 columnas y 5779 filas. La construcción de los modelos se realizó en plataforma Deductor. Cada modelo fue probado en el conjunto de 462 registros. Para todos los modelos se construyó la correspondiente matriz de clasificación y se calcularon los errores de 1º y 2º tipo, así como el error general de los modelos. En términos de minimizar estos errores, la regresión logística mostró los peores resultados y la red neuronal mostró los mejores. Además, se evaluó la efectividad de los modelos construidos según criterios de «ingresos» y «tiempo». Por el tiempo que cuesta el modelo de regresión logística supera a otros modelos. Sin embargo, en términos de ingresos, el modelo de red neuronal fue el mejor. Así, los resultados mostraron que para minimizar el tiempo dedicado al trabajo con los deudores es recomendable utilizar un modelo logístico. Sin embargo, para maximizar las ganancias y minimizar los errores de clasificación, es apropiado utilizar un modelo de red neuronal. Esto indica su eficacia y posibilidad de uso práctico en sistemas de puntuación inteligentes

Cognitive Modeling and Formation of the Knowledge Base of the Information System for Assessing the Rating of Enterprises

A mathematical model is proposed that makes it possible to describe in a conceptual and functional aspect the formation and application of a knowledge base (KB) for an intelligent information system (IIS). This IIS is developed to assess the financial condition (FC) of the company. Moreover, for circumstances related to the identification of individual weakly structured factors (signs). The proposed model makes it possible to increase the understanding of the analyzed economic processes related to the company\u27s financial system. An iterative algorithm for IIS has been developed that implements a model of cognitive modeling. The scientific novelty of the proposed approach lies in the fact that, unlike existing solutions, it is possible to adjust the structure of the algorithm depending on the characteristics of a particular company, as well as form the information basis for the process of assessing the company\u27s FC and the parameters of the cognitive model

Cognitive Modeling and Formation of the Knowledge Base of the Information System for Assessing the Rating of Enterprises

A mathematical model is proposed that makes it possible to describe in a conceptual and functional aspect the formation and application of a knowledge base (KB) for an intelligent information system (IIS). This IIS is developed to assess the financial condition (FC) of the company. Moreover, for circumstances related to the identification of individual weakly structured factors (signs). The proposed model makes it possible to increase the understanding of the analyzed economic processes related to the company's financial system. An iterative algorithm for IIS has been developed that implements a model of cognitive modeling. The scientific novelty of the proposed approach lies in the fact that, unlike existing solutions, it is possible to adjust the structure of the algorithm depending on the characteristics of a particular company, as well as form the information basis for the process of assessing the company's FC and the parameters of the cognitive model

Funcionalidades de la minería de datos

This document has been revised methodology and algorithms used to address a problem of prediction or cluster data according to the information requested. Data mining areas emerge database (data base), data warehouse (Data Warehouse) and large databases (Big Data), as a process of information extraction based on the mathematics and statistics. Being necessary to perform model selection, data exploration, data classification, prediction of valúes based on the data, the modeling of dependencies to solve the problem, the discovery of new rules and visualize the results, with so the analysis and interpretation of the information obtained is obtained. Some applications of data mining are: in education, in the media, in commerce, in the financial sector, in medicine, in agriculture, in social sciences, in public administration, and the technology. To made the extraction process request data, using some algorithm like, linear and logistic regression, Bayesian networks, naive Bayes, trees and decisión rules, logic and neural networks and fiizzy inference is requiredEn este documento se ha revisado una metodología y los algoritmos utilizados para abordar un problema de predicción o clúster de datos de acuerdo a la información solicitada. La minería de datos emerge de las áreas de base de datos (data base), repositorio de datos (Data Warehouse) y de las grandes bases de datos (big Data), como un proceso de extracción de información fundamentado en la matemáticas y la estadística. Siendo necesario realizar la selección del modelo, la exploración de los datos, la clasificación de datos, la predicción de valores en función de los datos, el modelamiento de las dependencias para resolver el problema, el descubrimiento de nuevas reglas y visualizar los resultados, con lo que se realiza el análisis e interpretación de la información obtenida.Es así, como algunas de las aplicaciones de la minería de datos son: en la educación, en la multimedia, en el comercio, en el sector financiero, en la medicina, en el sector agropecuario, en las ciencias sociales, en la gestión gubernamental, y en la tecnología. Para realizar el proceso de extracción de los datos solicitados de estas aplicaciones se requiere el uso de algunos algoritmos como los de regresión lineal, y logística, redes bayesianas, bayesnaive, árboles y reglas de decisión, lógica e inferencia difusa y redes neuronales

Three essays on the use of neural networks for financial prediction

The number of studies trying to explain the causes and consequences of the economic and financial crises usually rises considerably after a banking crisis occurs. The dramatic effects of the most recent financial crisis on the real economy around the world call for a better comprehension of previous crises as a way to anticipate future crisis episodes. It is precisely this objective, preventing future crises, the main motivation of this PhD dissertation. We identify two important mechanisms that have failed during the latest years and that are closely related to the onset of the financial crisis: The assessment of the solvency of banks along with the systemic risk over the time, and the detection of the macroeconomic imbalances in some countries, especially in Europe, which made the financial crisis evolve through a sovereign crisis. Our dissertation is made up of three different essays, trying to go a step ahead in the knowledge of these mechanisms.Departamento de Economía Financiera y ContabilidadDoctorado en Economía de la Empres

Machine learning no processo de risco de crédito das instituições bancárias

Uma vez que o sistema económico mundial se encontra em constante mudança, o estudo do risco de crédito tem uma grande importância para as instituições bancárias. Por estar associado a possíveis perdas que impactam o mercado financeiro, o processo de análise de crédito deve ser contínuo e progressivo. O atraso nos pagamentos de negócios tornou-se uma tendência, especialmente após as recentes crises financeiras. Desse modo, os bancos devem minimizar dívidas, analisar individualmente os créditos, agir com rapidez e se proteger de não pagamentos. Na mesma conjuntura, machine learning é uma tecnologia emergente para a construção de modelos analíticos, faz com que as máquinas aprendam com os dados. Com isso, efetuem análises preditivas de maneira mais rápida e eficiente. Fazer análises preditivas é muito importante e possui uma ampla gama de atuação para os bancos. Como, por exemplo: • Identificação dos melhores fatores de risco a serem utilizados na antecipação de crédito a clientes; • Obediência dos dispositivos legais; • Qualidade de dados; • Deteção de fraudes. Na criação de uma pontuação de risco de crédito bancário, automatizada, robusta e eficaz, machine learning vai ajudar na previsão da capacidade de crédito do cliente com mais precisão. O objetivo é analisar as diferentes abordagens de gestão de risco de crédito. Para tal, recorre-se a revisão de literatura de tópicos importantes, em destaque a machine learning, e ao uso de questionários. Os principais resultados mostraram que o uso de machine learning no risco de crédito bancário, ainda está em fase inicial. A maioria dos bancos já reconhece os valores que esta tecnologia pode proporcionar. Com base nesses resultados, os bancos que são tão sensíveis a mudanças, têm que sair do âmbito da teoria e investir em pequenos projetos. Só assim esta tecnologia provará a sua capacidade de melhoria, e transmitir a confiança necessária para este sector.As the global economic system is constantly changing, the study of credit risk is of great importance to banking institutions. Because it is associated with possible losses that impact the financial market, the process of credit analysis should be continuous and progressive. Late business payments have become a trend, especially after the recent financial crises. Thus, banks should minimize debt, analyze individual credits, act quickly and protect themselves from non-payment. At the same time, machine learning is an emerging technology for building analytical models, making machines learn from data. As a result, they carry out predictive analyses more quickly and efficiently. Predictive analysis is very important and has a wide range of activities for banks. For example: • Identification of the best risk factors to be used in anticipating credit to customers; • Compliance with legal provisions; • Obedience of legal provisions; • Data quality; • Fraud detection. In creating an automated, robust and effective bank credit risk score, machine learning will help predict the customer's creditworthiness more accurately. The goal is to analyze the different approaches to credit risk management. To this end, a literature review of important topics is used, especially machine learning and the use of questionnaires. The main results showed that the use of machine learning in bank credit risk is still at an early stage. Most banks already recognize the values that this technology can provide. Based on these results, banks that are so sensitive to change have to go beyond the scope of theory and invest in small projects. Only in this way will this technology prove its ability to improve and transmit the necessary confidence to this sector