Generic Architecture for Predictive Computational Modelling with Application to Financial Data Analysis: Integration of Semantic Approach and Machine Learning

The PhD thesis introduces a Generic Architecture for Predictive Computational Modelling capable of automating analytical conclusions regarding quantitative data structured as a data frame. The model involves heterogeneous data mining based on a semantic approach, graph-based methods (ontology, knowledge graphs, graph databases) and advanced machine learning methods. The main focus of my research is data pre-processing aimed at a more efficient selection of input features to the computational model. Since the model I propose is generic, it can be applied for data mining of all quantitative datasets (containing two-dimensional, size-mutable, heterogeneous tabular data); however, it is best suitable for highly interconnected data. To adapt this generic model to a specific use case, an Ontology as the formal conceptual representation for the relevant domain knowledge is needed. I have determined to use financial/market data for my use cases. In the course of practical experiments, the effectiveness of the PCM model application for the UK companies’ financial risk analysis and the FTSE100 market index forecasting was evaluated. The tests confirmed that the PCM model has more accurate outcomes than stand-alone traditional machine learning methods. By critically evaluating this architecture, I proved its validity and suggested directions for future research

Framework for data quality in knowledge discovery tasks

Actualmente la explosión de datos es tendencia en el universo digital debido a los avances en las tecnologías de la información. En este sentido, el descubrimiento de conocimiento y la minería de datos han ganado mayor importancia debido a la gran cantidad de datos disponibles. Para un exitoso proceso de descubrimiento de conocimiento, es necesario preparar los datos. Expertos afirman que la fase de preprocesamiento de datos toma entre un 50% a 70% del tiempo de un proceso de descubrimiento de conocimiento. Herramientas software basadas en populares metodologías para el descubrimiento de conocimiento ofrecen algoritmos para el preprocesamiento de los datos. Según el cuadrante mágico de Gartner de 2018 para ciencia de datos y plataformas de aprendizaje automático, KNIME, RapidMiner, SAS, Alteryx, y H20.ai son las mejores herramientas para el desucrimiento del conocimiento. Estas herramientas proporcionan diversas técnicas que facilitan la evaluación del conjunto de datos, sin embargo carecen de un proceso orientado al usuario que permita abordar los problemas en la calidad de datos. Adem´as, la selección de las técnicas adecuadas para la limpieza de datos es un problema para usuarios inexpertos, ya que estos no tienen claro cuales son los métodos más confiables. De esta forma, la presente tesis doctoral se enfoca en abordar los problemas antes mencionados mediante: (i) Un marco conceptual que ofrezca un proceso guiado para abordar los problemas de calidad en los datos en tareas de descubrimiento de conocimiento, (ii) un sistema de razonamiento basado en casos que recomiende los algoritmos adecuados para la limpieza de datos y (iii) una ontología que representa el conocimiento de los problemas de calidad en los datos y los algoritmos de limpieza de datos. Adicionalmente, esta ontología contribuye en la representacion formal de los casos y en la fase de adaptación, del sistema de razonamiento basado en casos.The creation and consumption of data continue to grow by leaps and bounds. Due to advances in Information and Communication Technologies (ICT), today the data explosion in the digital universe is a new trend. The Knowledge Discovery in Databases (KDD) gain importance due the abundance of data. For a successful process of knowledge discovery is necessary to make a data treatment. The experts affirm that preprocessing phase take the 50% to 70% of the total time of knowledge discovery process. Software tools based on Knowledge Discovery Methodologies offers algorithms for data preprocessing. According to Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms, KNIME, RapidMiner, SAS, Alteryx and H20.ai are the leader tools for knowledge discovery. These software tools provide different techniques and they facilitate the evaluation of data analysis, however, these software tools lack any kind of guidance as to which techniques can or should be used in which contexts. Consequently, the use of suitable data cleaning techniques is a headache for inexpert users. They have no idea which methods can be confidently used and often resort to trial and error. This thesis presents three contributions to address the mentioned problems: (i) A conceptual framework to provide the user a guidance to address data quality issues in knowledge discovery tasks, (ii) a Case-based reasoning system to recommend the suitable algorithms for data cleaning, and (iii) an Ontology that represent the knowledge in data quality issues and data cleaning methods. Also, this ontology supports the case-based reasoning system for case representation and reuse phase.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Fernando Fernández Rebollo.- Secretario: Gustavo Adolfo Ramírez.- Vocal: Juan Pedro Caraça-Valente Hernánde

Altman’s Z-Score Discriminant Analysis and Bankruptcy Assessment of Banks in Nigeria

This study aims to determine the distress level subsisting in the bridge banks set up by the Central Bank of Nigeria in 2011 to take over the nationalized banks; and the 2011-classified unsound banks using the Altman’s discriminant analysis model. Secondary data from four sampled classified distressed and unsound banks from the declared six for two years preceding their nationalization and two years after using the stratified sampling technique were analysed using the Alman Z-score discriminant analysis. Results shows that there are marginal improvements in the financial status of the sampled banks between 2010-2013 but they are still in a bankrupt position with Union Bank Plc, Wema Bank Plc, Keystone Bank Ltd and Mainstreet Bank Ltd having a Z-score of -0.56, 0.417, 1.5 and 0.45 respectively at 2013, all below the minimum threshold of 2.675 for classification of a bank as sound and non-bankrupt. This implies that the general broad-based monetary policy measures introduced by the CBN for the financially distressed bank are not much effective in resolving their financial crises in general, making necessary the introduction of bank-specific monetary and financial policies to solve identified bank-specific problems, and the CBN directly supervising these banks with daily monitoring of their operations

A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making

This paper is aimed at examining the role of annual reports’ sentiment in forecasting financial performance. The sentiment (tone, opinion) is assessed using several categorization schemes in order to explore various aspects of language used in the annual reports of U.S. companies. Further, we employ machine learning methods and neural networks to predict financial performance expressed in terms of the Z-score bankruptcy model. Eleven categories of sentiment (ranging from negative and positive to active and common) are used as the inputs of the prediction models. Support vector machines provide the highest forecasting accuracy. This evidence suggests that there exist non-linear relationships between the sentiment and financial performance. The results indicate that the sentiment information is an important forecasting determinant of financial performance and, thus, can be used to support decision-making process of corporate stakeholders

Credit scoring as an asset for decision making in intelligent decision support systems

Dissertação de mestrado em Engenharia de InformáticaRisk assessment is an important topic for financial institution nowadays, especially in the context of loan applications or loan requests and credit scoring. Some of these institutions have already implemented their own custom credit scoring systems to evaluate their clients’ risk supporting the loan application decision with this indicator. In fact, the information gathered by financial institutions constitutes a valuable source of data for the creation of information assets from which credit scoring mechanisms may be developed. Historically, most financial institutions support their decision mechanisms on regression algorithms, however, these algorithms are no longer considered the state of the art on decision algorithms. This fact has led to the interest on the research of new types of learning algorithms from machine learning able to deal with the credit scoring problem. The work presented in this dissertation has as an objective the evaluation of state of the art algorithms for credit decision proposing new optimization to improve their performance. In parallel, a suggestion system on credit scoring is also proposed in order to allow the perception of how algorithm produce decisions on clients’ loan applications, provide clients with a source of research on how to improve their chances of being granted with a loan and also develop client profiles that suit specific credit conditions and credit purposes. At last, all the components studied and developed are combined on a platform able to deal with the problem of credit scoring through an experts system implemented upon a multi-agent system. The use of multi-agent systems to solve complex problems in today’s world is not a new approach. Nevertheless, there has been a growing interest in using its properties in conjunction with machine learning and data mining techniques in order to build efficient systems. The work presented aims to demonstrate the viability and utility of this type of systems for the credit scoring problem.Hoje em dia, a análise de risco é um tópico importante para as instituições financeiras, especialmente no contexto de pedidos de empréstimo e de classificação de crédito. Algumas destas instituições têm já implementados sistemas de classificação de crédito personalizados para avaliar o risco dos seus clientes baseando a decisão do pedido de empréstimo neste indicador. De facto, a informação recolhida pelas instituições financeiras constitui uma valiosa fonte de dados para a criação de ativos de informação, de onde mecanismos de classificação de crédito podem ser desenvolvidos. Historicamente, a maioria das instituições financeiras baseia os seus mecanismos de decisão sobre algoritmos de regressão. No entanto, estes algoritmos já não são considerados o estado da arte em algoritmos de decisão. Este facto levou ao interesse na pesquisa de diferentes algoritmos de aprendizagem baseados em algoritmos de aprendizagem máquina, capaz de lidar com o problema de classificação de crédito. O trabalho apresentado nesta dissertação tem como objetivo avaliar o estado da arte em algoritmos de decisão de crédito, propondo novos conceitos de optimização que melhorem o seu desempenho. Paralelamente, um sistema de sugestão é proposto no âmbito do tema de decisão de crédito, de forma a possibilitar a perceção de como os algoritmos tomam decisões relativas a pedidos de crédito por parte de clientes, dotando-os de uma fonte de pesquisa sobre como melhorar as possibilidades de concessão de crédito e, ainda, elaborar perfis de clientes que se adequam a determinadas condições e propósitos de crédito. Por último, todos os componentes estudados e desenvolvidos são combinados numa plataforma capaz de lidar com o problema da classificação de crédito através de um sistema de especialistas, implementado como um sistema multi-agente. O uso de sistemas multi-agente para resolver problemas complexos no mundo de hoje não é uma nova abordagem. No entanto, tem havido um interesse crescente no uso das suas propriedades, em conjunto com técnicas de aprendizagem máquina e data mining para construir sistemas mais eficazes. O trabalho desenvolvido e aqui apresentado pretende demonstrar a viabilidade e utilidade do uso deste tipo de sistemas no problema de decisão de crédito