Financial distress model prediction using SVM +

Financial distress prediction is of great importance to all stakeholders in order to enable better decision-making in evaluating firms. In recent years, the rate of bankruptcy has risen and it is becoming harder to estimate as companies become more complex and the asymmetric information between banks and firms increases. Although a great variety of techniques have been applied along the years, no comprehensive method incorporating an holistic perspective had hitherto been considered. Recently, SVM+ a technique proposed by Vapnik [17] provides a formal way to incorporate privileged information onto the learning models improving generalization. By exploiting additional information to improve traditional inductive learning we propose a prediction model where data is naturally separated into several groups according to the size of the firm. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed model showed superior performance in terms of prediction accuracy in bankruptcy prediction and misclassification cost.This work was partially supported by Fundacao da Ciencia e Tecnologia' under grant no.PTDC/GES/70168/2006

Towards practical and provable domain adaptation

One of the most central questions in statistical modeling is how well a model will generalize. Absent strong assumptions we find that this question is difficult to answer in a meaningful way. In this work we seek to increase our understanding of the domain adaptation setting through two different lenses. First, we investigate whether tractably computable and tight generalization bounds on the performance of neural network classifiers exist in the current literature. The tightest bounds we find use a portion of the input data to tighten the gap between measured performance and the calculated bound. We present evaluations of four bounds using this tightening method on classifiers applied to image classification tasks: Two bounds from the literature in addition to two of our own construction. Further, we find that for situations lacking domain overlap, the existing literature lacks the tools to achieve tight, tractably computable bounds for the neural network models which we use. We conclude that a new approach might be needed. In the second part we therefore consider a setting where we change our underlying assumptions to ones which might be more plausible. This setting, based on learning using privileged information, is shown to result in consistent learning. We also show empirical gains over comparable methods when our assumptions are likely to hold, both in terms of performance and sample efficiency. In summary, the work set out herein has been a first step towards a better understanding of domain adaptation and how using data and new assumptions can help us further our knowledge about this topic

Self-adaptive MOEA feature selection for classification of bankruptcy prediction data

Article ID 314728Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved.This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy).The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier.This work was partially supported by the Portuguese Foundation for Science and Technology under Grant PEst-C/CTM/LA0025/2011 (Strategic Project-LA 25-2011-2012) and by the Spanish Ministerio de Ciencia e Innovacion, under the project "Gestion de movilidad efficiente y sostenible, MOVES" with Grant Reference TIN2011-28336