240 research outputs found
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
A revision of Altman’s Z- Score for SMEs: suggestions from the Italian Bankruptcy Law and pandemic perspectives
As the pandemic urged further investigations on the prediction of firms’ financial distress, this study develops and tests an alternative measure to the alert system elaborated by the NCCAAE which combines the benefits of the Z-score’s multivariate discriminant model with the background employed to develop the NCCAAE’ predictors. Using a sample of 43 viable and 43 non-viable Italian SMEs, we first compare the financial distress predictive accuracy of the NCCAAE’s alert system to that of the traditional Z-score over the period 2015-2019. On the basis of the results, we elaborate and compare the revised versions of both approaches which align the traditional Z-score to the current socio-economic conditions and provide an alternative measure to the NCCAAE’s alert system which embeds a Z-score calculated using the ratios elaborated by the NCCAAE for the alert system. The analysis of the two baseline approaches showed complementary results as the Z-score overperformed the alert system when predicting the status of non-viable firms whereas the opposite emerged as regards viable firms. The revised version of both approaches pointed out an enhanced predictive accuracy with respect to baseline models. In particular, the complementary role of the Z-score has been integrated into the new alert system as major contribute to its enhancement which pointed it out as the best measure employed. We, therefore, contribute to the literature studying the financial distress prediction developments by elaborating an alternative measure to the alert system developed by the NCCAAE which combines the benefits of the Z-score’s multivariate discriminant function with the background employed to develop the NCCAAE’ predictors. Our analysis enriches the post-pandemic debate on refined financial distressed prediction methods by pointing out the limits of the alert system as designed by the NCCAAE and suggests an alternative and better performing measure that may be used by third-party bodies to predict financial distress
WEBIC: a web based business insolvency classifier using neural networks
Business insolvency is one of the major problems faced by decision makers, especially to detect the early symptom that may contribute to critical business condition.This paper discusses the implementation of neural networks in classifying business insolvency cases in Malaysia. The developed prototype can be accessed remotely via World Wide Web (WWW).For the development purposes, the data was obtained from the Registrar of Business / Companies (ROB/ROC), Kuala Lumpur Stock Exchange and Bank Negara Malaysia (Central Bank of
Malaysia).Several experiments were conducted to determine the most suitable parameters for the neural network model.Based on the experimental results, a network with an architecture of 11-6-1 with learning rate 0.1 and momentum term of 0.5. The prototype obtained 90.25% generalization
and therefore indicates that the prototype has
the potential to be used as a tool for classifying business insolvency.Hence, the prototype provides a basic framework for developing such a classifie
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Predicting business failure using artificial intelligence system
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPredicting business insolvency is considered one of the main supportive sources of information
for decision making for financial institutions, investors, creditors, and other participants in the
business market. Financial reporting systems provide relevant information that can be used to
assess the financial position of firms. It is crucial to have classification and prediction models
that can analyse this financial information and provide accurate assurance for users about
business health. Recent studies have explored the use of machine learning tools as substitute
for traditional statistical methods to develop classification models to classify firm insolvency
according to financial statement information. However, these models have no ideal classifier,
since each provides a certain percentage of wrong outputs, which is a crucial consideration;
every percentage of wrong response can mean massive financial losses for stakeholders.
Therefore, this study proposes new insolvency classification and perdition models based on
machine learning modelling techniques to develop an improved classifier.
Individual modelling techniques using statistical methods and machine learning were used to
develop the classification model of business insolvency. The results showed that machine
learning method outperformed statistical methods. Deep Learning (DPL) achieved the highest
performance based on all performance measurements used in the study, and it was the best
individual classifier, with average accuracy of 97.2% using all-years dataset. Ensemble-
Boosted Decision Tree classifier ranked second, followed by Decision Tree classifier. Thus, it
has been proven that DPL modelling approach is useful for business insolvency classification.
A key contribution in enhancing individual classifier outputs is the use of traditional combining
methods with two new aggregation methods in business insolvency (Fuzzy Logic and
Consensus Approach). The Consensus Approach showed the best improvement in the results
of all individual classifiers with average accuracy of 97.7%, and it is considered the best
classification method not only in comparison with individual classifiers, but also with
traditional combiners.
This study pioneers the development of a time series business insolvency prediction model
with Big Data for UK businesses. The aim of the model is to provide early prediction about a
business health. Three prediction models were developed based on Nonlinear Autoregressive
with Exogenous Input models (NARX), Nonlinear Autoregressive Neural Network (NAR),
and Deep Learning Time-series model (DPL-SA) and achieved average accuracy rates of
83.6%, 89.5%, and 91.35%, respectively. The results show relatively high performance in
comparison with the best individual classifier (deep learning)
WEBIC: a web based business insolvency classifier using neural networks
Business insolvency is one of the major problems faced by decision makers, especially to detect the early symptom that may contribute to critical business condition.This paper discusses the implementation of neural networks in classifying business insolvency cases in Malaysia. The developed prototype can be accessed remotely via World Wide Web (WWW).For the development purposes, the data was obtained from the Registrar of Business / Companies (ROB/ROC), Kuala Lumpur Stock Exchange and Bank Negara Malaysia (Central Bank of
Malaysia).Several experiments were conducted to determine the most suitable parameters for the neural network model.Based on the experimental results, a network with an architecture of 11-6-1 with learning rate 0.1 and momentum term of 0.5. The prototype obtained 90.25% generalization
and therefore indicates that the prototype has
the potential to be used as a tool for classifying business insolvency.Hence, the prototype provides a basic framework for developing such a classifie
An insight into the experimental design for credit risk and corporate bankruptcy prediction systems
Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more robust decision-making systems capable of managing and analyzing complex data. As a result, plentiful techniques have been developed with the aim of producing accurate prediction models that are able to tackle these issues. However, the design of experiments to assess and compare these models has attracted little attention so far, even though it plays an important role in validating and supporting the theoretical evidence of performance. The experimental design should be done carefully for the results to hold significance; otherwise, it might be a potential source of misleading and contradictory conclusions about the benefits of using a particular prediction system. In this work, we review more than 140 papers published in refereed journals within the period 2000–2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications. We provide some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures in order to converge on a systematic, consistent validation standard.This work has partially been supported by the Mexican Science and Technology Council (CONACYT-Mexico) through a Postdoctoral Fellowship [223351], the Spanish Ministry of Economy under grant TIN2013-46522-P and the Generalitat Valenciana under grant PROMETEOII/2014/062
Corporate Bankruptcy Prediction
Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy
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