Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Towards Emotional Interaction: Using Movies to Automatically Learn Users' Emotional States

The HCI community is actively seeking novel methodologies to gain insight into the user’s experience during interaction with both the application and the content. We propose an emotional recognition engine capable of automatically recognizing a set of human emotional states using psychophysiological measures of the autonomous nervous system, including galvanic skin response, respiration, and heart rate. A novel pattern recognition system, based on discriminant analysis and support vector machine classifiers is trained using movies’ scenes selected to induce emotions ranging from the positive to the negative valence dimension, including happiness, anger, disgust, sadness, and fear. In this paper we introduce an emotion recognition system and evaluate its accuracy by presenting the results of an experiment conducted with three physiologic sensors.

Residents’ perceptions of tourism impacts in Guimarães (Portugal): a cluster analysis

In this investigation, a cluster analysis was used to separate Guimara˜es (Portugal) residents into clusters according to their perceptions of the impacts of tourism development. This approach is uncommonly applied to Portugal data and is even rarer for world heritage sites. The world heritage designation is believed to make an area more attractive to tourists. The clustering procedure analysed 400 data observations from a Guimara˜es resident survey and revealed the existence of three clusters: the Sceptics, the Moderately Optimistic and the Enthusiasts. The results were consistent with the empirical literature’s results, with the emergent nature of the destination found to be relevant. The fact that tourism is relatively recent in this destination has its major reflex in the devaluation by most of the residents of the negative impacts of tourism development.

Guimarães residents’ perceptions towards tourism impacts: a cluster analysis

The present study was designed to assess and segment local residents with respect to their perceived impacts of Guimarães tourism development. The residents of this municipality (located in the northern part of Portugal) are quite strong in their support to tourism. However, they do not keep a homogeneous perception of tourism impacts. A clusters analysis using data from a survey of 400 Guimarães residents’ has revealed the existence of three clusters, according the different degrees of perceived tourism impacts: the Skeptics - moderate in relation to the benefits (averages range from 2.89-3.74) and the ones more concerned with its costs (averages range from 2.86-3.74); the Moderately optimistic - very optimistic about the benefits of tourism (averages range from 3.74-4.51) and conscious of the costs (averages range from 2.71-3.49); the Enthusiasts - very optimistic about tourism benefits (averages range from 2.92-4.52) and little worried about its costs (averages range from 1.78-3.26). Following the data from the survey, the findings are discussed and a few conclusions are extracted.