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

Soft computing techniques applied to finance

Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad

A Study of Panel Logit Model and Adaptive Neuro-Fuzzy Inference System in the Prediction of Financial Distress Periods

The purpose of this paper is to present two different approaches of financial distress pre-warning models appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) market from 2002 through 2008. We present a binary logistic regression with paned data analysis. With the pooled binary logistic regression we build a model including more variables in the regression than with random effects, while the in-sample and out-sample forecasting performance is higher in random effects estimation than in pooled regression. On the other hand we estimate an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell (Gbell) functions and we find that ANFIS outperforms significant Logit regressions in both in-sample and out-of-sample periods, indicating that ANFIS is a more appropriate tool for financial risk managers and for the economic policy makers in central banks and national statistical services

Predicting Financial Distress Within Indian Enterprises: A Comparative Study on the Neuro-Fuzzy Models and the Traditional Models of Bankruptcy Prediction

The financial distresses is of major importance in the financial management system particularly in the case of this competitive environs. There are several traditional methods existing for predicting the financial distress within the country. Major factors influencing the financial distress is the stock market, credit risk and so on. Hence there is a need of models which could make dynamic predictions with the use of dynamic variables. There are several machine learning and artificial intelligence-based bankruptcy prediction models available. The neural network concepts and the computational intelligence-based methods are highly acceptable in the prediction arena. This research presents a comprehensive review of the existing prediction approaches and suggests future research directions and ideas. Some of the existing methods are support vector machines, artificial neural network, multi-layer perceptron, and the linear models such as principal component analysis. Neuro-fuzzy approaches, Deep belief neural networks, Convolution neural networks are also discussed

A New Approach to Adaptive Neuro-fuzzy Modeling using Kernel based Clustering

Data clustering is a well known technique for fuzzy model identification or fuzzy modelling for apprehending the system behavior in the form of fuzzy if-then rules based on experimental data Fuzzy c- Means FCM clustering and subtractive clustering SC are efficient techniques for fuzzy rule extraction in fuzzy modeling of Adaptive Neuro-fuzzy Inference System ANFIS In this paper we have employed a novel technique to build the rule base of ANFIS based on the kernel based variants of these two clustering techniques which have shown better clustering accuracy In kernel based clustering approach the kernel functions are used to calculate the distance measure between the data points during clustering which enables to map the data to a higher dimensional space This generalization makes data set more distinctly separable which results in more accurate cluster centers and therefore a more precise rule base for the ANFIS can be constructed which increases the prediction performance of the system The performance analysis of ANFIS models built using kernel based FCM and kernel based SC has been done on three business prediction problems viz sales forecasting stock price prediction and qualitative bankruptcy prediction A performance comparison with the ANFIS models based on conventional SC and FCM clustering for each of these forecasting problems has been provided and discusse

МОДЕЛІ ПРОГНОЗУВАННЯ В МЕХАНІЗМІ РАННЬОГО ІНФОРМУВАННЯ І ПОПЕРЕДЖЕННЯ ФІНАНСОВИХ КРИЗ У КОРПОРАТИВНИХ СИСТЕМАХ

The paper is devoted to the problem of preventing financial crises in corporate systems, whose activities are becoming more and more complex in the context of globalization. The mechanism of early informing and crisis prevention in corporate systems is proposed, and includes five main modules: an analysis of the financial condition of the corporation, an analysis of the financial condition of subsidiaries, an evaluation of the impact of the financial crisis on a subsidiary on the threat of bankruptcy of the corporation as a whole, forecasting the financial condition of subsidiaries and corporation as a whole, anti-crisis management. The first four modules of the mechanism are the modules of implementation of proactive crisis management in the corporation, aimed at preventing the emergence of a crisis state, both in individual elements and in the corporate system as a whole. The fifth module is used in conditions of the current negative estimation of the financial condition of the corporation, and it is a "response" to existing crisis processes and phenomena in the corporation. After its implementation during the process of monitoring of the financial condition, proactive control modules are started to be used to allow early diagnosis and to prevent a crisis state. Particular attention is paid to such modules of proactive management as the evaluation of the impact of financial crises of subsidiaries on bankruptcy of the corporations as a whole and forecasting financial crises. A model basis for these two modules was developed. Neural networks, the mathematical apparatus of fuzzy logic, and the Caterpillar method were used for developing the models of estimation of the crisis threat in the corporate system. The developed set of models allowed to estimate the threat of financial crises in the parent enterprise and in the subsidiaries of the corporation, not only in the current but also in the perspective periods. The obtained results indicate that the financial condition of the investigated corporation is characterized by low level of the bankruptcy threat. Along with this, there is an increase in the threat of bankruptcy in a number of subsidiaries in the perspective period and the strong impact of local crises on the financial position of the corporation as a whole. The latter leads to the need of implementation of the anti-crisis measures in the corporate structure. An adequate tool for choosing anti-crisis measures and developing scenarios for the implementation of the anti-crisis management strategy is simulation modelling based on the concept of system dynamics.Рассматривается проблема предупреждения финансовых кризисов в корпоративных системах, деятельность которых становится все более сложной в контексте глобализации. Особое внимание уделяется оценке влияния финансовых кризисов дочерних компаний на банкротство корпораций в целом. Для оценки угрозы кризисов в корпоративной системе используются нейронные сети, математический аппарат нечеткой логики, метод «Caterpillar».Розглядається проблема запобігання фінансовим кризам у корпоративних системах, діяльність яких стає дедалі складнішою в контексті глобалізації. Запропоновано механізм раннього інформування і попередження криз у корпоративних системах, який включає п’ять основних модулів: аналіз фінансового стану корпорації, аналіз фінансового стану дочірніх підприємств, оцінка впливу фінансової кризи на дочірньому підприємстві на загрозу банкрутства корпорації в цілому, прогнозування фінансового стану дочірніх підприємств і корпорації в цілому, антикризове управління. Перші чотири модулі механізму є модулями реалізації проактивного антикризового управління в корпорації, спрямованого на недопущення появи кризового стану як в окремих елементах, так і корпоративної системі в цілому. П’ятий модуль використовується при поточній негативній оцінці стану корпорації і є «реакцією» на вже наявні кризові процеси і явища в корпорації. Після його реалізації у процесі моніторингу стану застосовуються модулі проактивного управління, що дозволяють здійснювати ранню діагностику і попереджати кризовий стан. Особливу увагу приділено таким модулям проактивного управління, як оцінка впливу фінансових криз дочірніх компаній на банкрутство корпорацій у цілому, прогнозування фінансових криз. Розроблено модельний базис цих двох модулів. Для побудови моделей оцінки загрози кризи корпоративної системи використовуються нейронні мережі, математичний апарат нечіткої логіки, метод «Caterpillar». Розроблений комплекс моделей дозволив оцінити загрозу формування фінансових криз на головному і дочірніх підприємствах корпорації не тільки в поточному періоді, а й у перспективному. Отримані результати свідчать, що фінансовий стан досліджуваної корпорації характеризується низьким рівнем загрози банкрутства. Поряд із цим спостерігається посилення загрози банкрутства на низці дочірніх підприємств у перспективному періоді і сильний вплив локальних криз на фінансовий стан корпорації в цілому. Останнє призводить до необхідності здійснення антикризових заходів у корпоративній структурі. Адекватним інструментом вибору антикризових заходів і формування сценаріїв реалізації стратегії антикризового управління є імітаційне моделювання, засноване на концепції системної динаміки

Prediction of Banks Financial Distress

In this research we conduct a comprehensive review on the existing literature of prediction techniques that have been used to assist on prediction of the bank distress. We categorized the review results on the groups depending on the prediction techniques method, our categorization started by firstly using time factors of the founded literature, so we mark the literature founded in the period (1990-2010) as history of prediction techniques, and after this period until 2013 as recent prediction techniques and then presented the strengths and weaknesses of both. We came out by the fact that there was no specific type fit with all bank distress issue although we found that intelligent hybrid techniques considered the most candidates methods in term of accuracy and reputatio

Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA

In this study two approaches are applied for the prediction of the economic recession or expansion periods in USA. The first approach includes Logit and Probit models and the second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell membership functions. The in-sample period 1950-2006 is examined and the forecasting performance of the two approaches is evaluated during the out-of sample period 2007-2010. The estimation results show that the ANFIS model outperforms the Logit and Probit model. This indicates that neuro-fuzzy model provides a better and more reliable signal on whether or not a financial crisis will take place.ANFIS, Discrete Choice Models, Error Back-propagation, Financial Crisis, Fuzzy Logic, US Economy

The Impacts of Machine Learning in Financial Crisis Prediction

The most complicated and expected issue to be handled in corporate firms, small-scale businesses, and investors’ even governments are financial crisis prediction. To this effect, it was of interest to us to investigate the current impact of the newly employed technique that is machine learning (ML) to handle this menace in all spheres of business both private and public. The study uses systematic literature assessment to study the impact of ML in financial crisis prediction. From the selected works of literature, we have been able to establish the important role play by this method in the prediction of bankruptcy and creditworthiness that was not handled appropriately by others method. Also, machine learning helps in data handling, data privacy, and confidentiality. This study presents a leading approach to achieving financial growth and plasticity in corporate organizations. We, therefore, recommend a real-time study to investigate the impact of ML in FCP. &nbsp