Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

An artificial neural network approach for assigning rating judgements to Italian Small Firms

Based on new regulations of Basel II Accord in 2004, banks and financial nstitutions have now the possibility to develop internal rating systems with the aim of correctly udging financial health status of firms. This study analyses the situation of Italian small firms that are difficult to judge because their economic and financial data are often not available. The intend of this work is to propose a simulation framework to give a rating judgements to firms presenting poor financial information. The model assigns a rating judgement that is a simulated counterpart of that done by Bureau van Dijk-K Finance (BvD). Assigning rating score to small firms with problem of poor availability of financial data is really problematic. Nevertheless, in Italy the majority of firms are small and there is not a law that requires to firms to deposit balance-sheet in a detailed form. For this reason the model proposed in this work is a three-layer framework that allows us to assign ating judgements to small enterprises using simple balance-sheet data.rating judgements, artificial neural networks, feature selection

Statistical modelling to predict corporate default for Brazilian companies in the context of Basel II using a new set of financial ratios

This paper deals with statistical modelling to predict failure of Brazilian companies in the light of the Basel II definition of default using a new set of explanatory variables. A rearrangement in the official format of the Balance Sheet is put forward. From this rearrangement a framework of complementary non-conventional ratios is proposed. Initially, a model using 22 traditional ratios is constructed. Problems associated with multicollinearity were found in this model. Adding a group of 6 non-conventional ratios alongside traditional ratios improves the model substantially. The main findings in this study are: (a) logistic regression performs well in the context of Basel II, yielding a sound model applicable in the decision making process; (b) the complementary list of financial ratios plays a critical role in the model proposed; (c) the variables selected in the model show that when current assets and current liabilities are split into two sub-groups - financial and operational - they are more effective in explaining default than the traditional ratios associated with liquidity; and (d) those variables also indicate that high interest rates in Brazil adversely affect the performance of those companies which have a higher dependency on borrowing

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Financial crises and bank failures: a review of prediction methods

In this article we provide a summary of empirical results obtained in several economics and operations research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults, as well as outlines of the methodologies used. We analyze financial and economic circumstances associated with the US subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. The intent of the article is to promote future empirical research that might help to prevent bank failures and financial crises.financial crises; banking failures; operations research; early warning methods; leading indicators; subprime markets

Financial crises and bank failures: a review of prediction methods

In this article we analyze financial and economic circumstances associated with the U.S. subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. We suggest that the level of cross-border holdings of long-term securities between the United States and the rest of the world may indicate a direct link between the turmoil in the securitized market originated in the United States and that in other countries. We provide a summary of empirical results obtained in several Economics and Operations Research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults; we also extensively outline the methodologies used in them. The intent of this article is to promote future empirical research for preventing financial crises.Subprime mortgage ; Financial crises

Predicting Corporate Bankruptcy: Lessons from the Past

The need for corporate bankruptcy prediction models arises in 1960 after the increase in incidence of some major bankruptcies. Over the years, the episodes of financial turmoil increase in number and so does these bankruptcy prediction models. Existing reviews of bankruptcy models are either narrowly focused or outdated. Current study aims to provide an overview of the existing models for predicting bankruptcy and review the significance of these models. Furthermore, it highlights the problems and issues in the existing models which hinders the accuracy in predicting bankruptcy