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

Semantic Data Pre-Processing for Machine Learning Based Bankruptcy Prediction Computational Model

This paper studies a Bankruptcy Prediction Computational Model (BPCM model) – a comprehensive methodology of evaluating companies’ bankruptcy level, which combines storing, structuring and pre-processing of raw financial data using semantic methods with machine learning analysis techniques. Raw financial data are interconnected, diverse, often potentially inconsistent, and open to duplication. The main goal of our research is to develop data pre-processing techniques, where ontologies play a central role. We show how ontologies are used to extract and integrate information from different sources, prepare data for further processing, and enable communication in natural language. Using ontology, we give meaning to the disparate and raw business data, build logical relationships between data in various formats and sources and establish relevant context. Our Ontology of Bankruptcy Prediction (OBP Ontology) which provides a conceptual framework for companies’ financial analysis, is built in the widely established Prote ́ge ́ environment. An OBP Ontology can be effectively described with a graph database. Graph database expands the capabilities of traditional databases tackling the interconnected nature of economic data and providing graph-based structures to store information allowing the effective selection of the most relevant input features for the machine learning algorithm. To create and manage the BPCM Graph Database (Graph DB), we use the Neo4j environment and Neo4j query language, Cypher, to perform feature selection of the structured data. Selected key features are used for the Machine Learning Engine – supervised MLP Neural Network with Sigmoid activation function. The programming of this component is performed in Python. We illustrate the approach and advantages of semantic data pre-processing applying it to a representative use case

Secured Credit and Bankruptcy: A Call for the Federalization of Personal Property Security Law

In recent years, the need for systems monitoring the current in ation pressure in pneumatic tires has grown dramatically. One way to monitor the in ation pressure is to use the fact that the tire reacts like a spring when excited from road roughness. The resonance frequency of the tire can be estimated with standard signal processing procedures. Three different approaches for vibration analysis are studied using a simulation model similar to the tire model. The first approach uses the raw wheel speed which is highly over-sampled. In the second approach a pre-filter is used to remove the disturbances and the third approach uses down sampling to isolate the vibration frequency. Especially bias in the estimation is studied

Sovereign Debt Restructuring: A Model-Law Approach

The existing contractual framework for sovereign debt restructuring is sorely inadequate. Whether or not their fault, nations sometimes take on debt burdens that become unsustainable. Until resolved, the resulting sovereign debt problem hurts not only those nations (such as Greece) but also their citizens, their creditors, and—by posing serious systemic risks to the international financial system—the wider economic community. The existing contractual framework functions poorly to resolve the problem because it often leaves little alternative between a sovereign debt bailout, which is costly and creates moral hazard, and a default, which raises the specter of systemic financial contagion. Most observers therefore want to strengthen the legal framework for resolving sovereign debt problems. International organizations, including the United Nations, have been contemplating strengthening that framework through treaties. The political economy of treaty-making, however, makes that approach highly unlikely to succeed in the near future. This article argues, in contrast, that a model-law approach should not only strengthen that legal framework but also should be politically and economically feasible. Model laws have long been used in cross-border lawmaking, but they are different than treaties. Unlike a treaty, a model law would not require general acceptance for its implementation. Only one or two jurisdictions, for example, need enact the text of this article’s proposed model law for it to become widely effective. Once that occurs, a debtor-state whose debt contracts are governed by those jurisdictions’ laws, or by its own laws, could restructure that debt without needing to amend any of those contracts. A model-law approach should also be desirable. This article’s model law, for example, would reduce uncertainty and should also achieve significant cost advantages—both to debtor-states and to their creditors—over the sovereign-debt-restructuring status quo. Because it would require only a ministerial supervisory process, the model law would not interfere with the exercise of a sovereign’s political discretion. Moreover, the model law provides incentives to motivate fair bargaining on behalf of debtor-states and their creditors, while restricting rent-seeking holdouts. It also enables the type of interim funding of day-to-day debts that a debtor-state needs during its debt restructuring. Debtor-states should therefore want (and creditors, other than rent-seeking holdouts, should want them) to enact into law this article’s proposed model-law text. Regardless of whether that enactment occurs, however, the article should serve its underlying purpose: to provide a conceptual and legal analysis of how a model law could be structured and how a model-law approach could be used to solve the problem of unsustainable sovereign debt burdens, and to help develop the norms required to facilitate those goals