On the utility of input selection and pruning for financial distress prediction models

Analyzes the use of linear and neural network models for financial distress classification, with emphasis on the issues of input variable selection and model pruning. A data-driven method for selecting input variables (financial ratios, in this case) is proposed. A case study involving 60 British firms in the period 1997-2000 is used for illustration. It is shown that the use of the Optimal Brain Damage pruning technique can considerably improve the generalization ability of a neural model. Moreover, the set of financial ratios obtained with the proposed selection procedure is shown to be an appropriate alternative to the ratios usually employed by practitioners.

Variable selection for financial distress classification using a genetic algorithm

This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature.

Neural network basis function center selection using cluster analysis

This paper deals with the selection of centres for radial basis function (RBF) networks. A novel mean-tracking clustering algorithm is described as a way in which centers can be chosen based on a batch of collected data. A direct comparison is made between the mean-tracking algorithm and k-means clustering and it is shown how mean-tracking clustering is significantly better in terms of achieving an RBF network which performs accurate function modelling.

Stimulus-driven selection of routes to imitation

Several models have proposed that an action can be imitated via one of two routes: a direct visuospatial route, which can in principle mediate imitation of both meaningful (MF) and meaningless (ML) actions, and an indirect semantic route, which can be used only for MF actions. The present study investigated whether selection between the direct and indirect routes is strategic or stimulus driven. Tessari and Rumiati (J Exp Psychol Hum Percept Perform 30:1107–1116, 2004) have previously shown, using accuracy measures, that imitation of MF actions is superior to imitation of ML actions when the two action types are presented in separate blocks, and that the advantage of MF over ML items is smaller or absent when they are presented in mixed blocks. We first replicated this finding using an automated reaction time (RT), as well as accuracy, measure. We then examined imitation of MF and ML actions in the mixed condition as a function of the action type presented in the previous trial and in relation to the number of previous test trials. These analyses showed that (1) for both action types, performance was worse immediately after ML than MF trials, and (2) even at the beginning of the mixed condition, responding to MF actions was no better than responding to ML items. These results suggest that the properties of the action stimulus play a substantial role in determining whether imitation is mediated by the direct or the indirect route, and that effects of block composition on imitation need not be generated through strategic switching between routes.