Failure prediction of dotcom companies using neural network–genetic programming hybrids

Ravisankar, P.; Ravi, Vadlamani; Bose, Indranil

doi:10.1016/j.ins.2009.12.022

Cited by 54 publications

(28 citation statements)

References 50 publications

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“…The significance of CFFOTA and CFFOCL provides support for this postulate. Furthermore, CFFOTA, which has been reported significant in several other studies in different contexts (Shumway, 2001, Bose, 2006, Ravisankar et al, 2010 had by far the largest estimated effect. CFFOCL, also reported significant by Gilbert et al (1990), had the second largest effect.…”

Section: Discussionmentioning

confidence: 64%

Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling

Khoja

Chipulu

Jayasekera

2014

Rev Quant Finan Acc

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Design/methodology/approach: Financial ratio data on 56 matched pairs of insolvent and solvent firms are analysed using logistic regression with best-subset selection criteria to identify significant ratios, and prediction accuracy is tested on an ex-ante sample. The main dimensions of ratios, and the weights that firms attach to them, are examined using 3-way Multidimensional Scaling (MDS).Findings: A parsimonious Logit model comprising one profitability, one leverage and two cash flow ratios has accuracy levels of 84.4% overall, 95.6% type I and 73.9% type II. Four financial-ratio dimensions are extracted from the MDS: (i) 'Non-strategic sales activities', (ii) 'Profitability and financial stability balance', (iii) 'Sales activities against capital conversion'; and (iv) 'Market value against cash generation'. Insolvent firms appear very specific and attach most salience to the 'Non-strategic sales activities' dimension; unlike solvent firms which attach more salience to the other three dimensions.Practical Implications: Results indicate profitability ratios should be included in GCC insolvency classification models. It is suggested that firms' managers should focus less on non-strategic sales activities to reduce susceptibility to insolvency. Originality/value:The study provides empirical evidence on insolvency in the GCC and introduces the application of 3-way MDS to insolvency research in the region.

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Section: Discussionmentioning

confidence: 64%

Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling

Khoja

Chipulu

Jayasekera

2014

Rev Quant Finan Acc

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“…The target variable can be continuous (typically a general prediction problem and usually approached by a type of regression model) or categorical (classification problem). The most widely used methods belonging to this group include decision trees [29], neural networks [19] and support vector machines [26].…”

Section: Machine Learning In Financial Risk Analysismentioning

confidence: 99%

“…While in case of the problem of crisis prediction, a class variable is usually present, unsupervised learning methods can still offer an important tool, with the most frequently used methods belonging to this group include selforganizing maps [23] and c-means clustering [12] In a recent and literature review, a complete picture of the application of machine learning in financial crisis prediction was given by Lin et al [11]. Additionally to the above mentioned two groups, they found statistics-based learning methods, for example logistic regression [24] or discriminant analysis [18], and other methods, such as genetic algorithms [19], as widely used in the crisis prediction literature.…”

Section: Machine Learning In Financial Risk Analysismentioning

confidence: 99%

Possibilistic Clustering for Crisis Prediction: Systemic Risk States and Membership Degrees

Mezei

Sarlin

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Research on understanding and predicting systemic financial risk has been of increasing importance in the recent years. A common approach is to build predictive models based on macro-financial vulnerability indicators to identify systemic risk at an early stage. In this article, we outline an approach for identifying different systemic risk states through possibilistic fuzzy clustering. Instead of directly using a supervised classification method, we aim at identifying coherent groups of vulnerability with macrofinancial indicators for pre-crisis data, and determine the level of risk for a new observation based on its similarity to the identified groups. The approach allows for differentiating among different possible pre-crisis states, and using this information for estimating the possibility of systemic risk. In this work, we compare different fuzzy clustering methods, as well as conduct an empirical exercise for European systemic banking crises.

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“…Another problem refers to the fact that several works employ this parametric test to compare multiple algorithms (e.g. Ravisankar et al 2010;Tsai and Wu 2008;Ribeiro et al 2012), even though not being suitable to carry out this type of comparisons.…”

Section: Statistical Tests Of Significancementioning

confidence: 99%

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

2014

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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.

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Failure prediction of dotcom companies using neural network–genetic programming hybrids

Cited by 54 publications

References 50 publications

Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling

Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling

Possibilistic Clustering for Crisis Prediction: Systemic Risk States and Membership Degrees

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

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