The Globe and Mail’s Report on Business annually publishes governance rankings for more than 200 companies represented in the TSX/S&P index. There are four sub-categories that comprise the composite scores: board composition; board and CEO compensation; shareholder rights; and board governance disclosure. The purpose of this paper is to examine the association between the composite or sub-category corporate governance scores and various measures of firm value. We test for this association using data for 2002 through 2005 on the Report on Business rankings and various financial and market measures. Overall, our study does not find an association between the composite or subcategory corporate governance scores and the various measures of firm value.

This paper investigates the performance of Artificial Neural Networks for the classification and subsequent prediction of business entities into failed and nonfailed classes. Two techniques, back-propagation and Optimal Estimation Theory (OET), are used to train the neural networks to predict bankruptcy filings. The data are drawn from Compustat data tapes representing a cross-section of industries. The results obtained with the neural networks are compared with other well-known bankruptcy prediction techniques such as discriminant analysis, probit and logit, as well as against benchmarks provided by directly applying the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980) to our data set. We control the degree of 'disproportionate sampling' by creating 'training' and 'testing' populations with proportions of bankrupt firms ranging from 1% to 50%. For each population, we apply each technique 50 times to determine stable accuracy rates in terms of Type I, Type II and Total Error. We show that the performance of various classification techniques, in terms of their classification errors, depends on the proportions of bankrupt firms in the training and testing data sets, the variables used in the models, and assumptions about the relative costs of Type I and Type II errors. The neural network solutions do not achieve the 'magical' results that literature in this field often promises, although there are notable 'pockets' of superior performance by the neural networks, depending on particular combinations of proportions of bankrupt firms in training and testing data sets and assumptions about the relative costs of Type I and Type II errors. However, since we tested only one architecture for the neural network, it will be necessary to investigate potential improvements in neural network performance through systematic changes in neural network architecture.

This paper describes and compares six procedures that can be used in a regression model to adjust for outliers in the data and nonlinearities in the relationship between the dependent and independent variables. The data accommodation procedures are: (1) noadjustment; (2) winsorizing ; (3) trimming; (4) regression on ranks; (5) nonlinear regression; and (6) piecewise linear regression. The results show that the choice of data accommodation procedure has a major impact on the predictive ability and coeficient estimates of the regression model. The winsorizing and ranking procedures produce a regression model that jits the data well and has a low level of prediction error.Decision models based on financial variables have been applied in a variety of situations, including predicting bankruptcy (Altman [ 1968]), explaining and estimating firm systematic risk (Beaver, Kettler, and Scholes [ 1970]), predicting bond ratings (Pinches and Mingo [ 1973]), and in evaluating the information content of accrual versus cash flow measures (Bowen, Burgstahler and Daley [1987]). When constructing such a model, a researcher is confronted with several decisions that can have a dramatic and distortive impact on the results. A researcher must decide which variables are "important" in explaining the dependent variable, and then choose surrogates to represent these variables. The researcher also must determine the functional relationship between the dependent and independent variables.

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Abstract. Prior studies have examined whether audit opinions have incremental explanator>' power over financial statement data in predicting bankruptcy filings. However, recent regulatory pronouncements indicate that the auditor should attempt to predict impending financial distress (going-concern difficulties), not whether a firm will file for bankruptcy. This study compares the audit opinion to the resolution of a bankruptcy filing to determine whether prior claims of audit failures might be due to the auditor's focus on financial distress resolution rather than the act of filing for bankruptcy. We find that the audit opinion is a significant variable in a model explaining the resolution of a bankruptcy filing. However, the audit opinion did not predict resolution of bankruptcy proceedings with any greater accuracy than did a naive mechanical model.Resume. Des chercheurs se sont deja demande si I'opinion des verificateurs avait un pouvoir explicatif marginal par rapport aux donnees des etats financiers dans la prediction des depots de biian. Or, les reglements recemment promulgues prevoient que les verificateurs doivent tenter de predire !es difficuttes fmancieres imminentes (menaces a la permanence de I'entreprise), et non pas les depots de bilan, Les auteurs mettent en paralleic {'opinion du verificateur et Tissue des depots de bilan afin de determiner si les allegations formulees d'inaptitude des verificateurs peuvent etre attribuables a I'interet porte par le verificateur a la resolution des difficultes financieres de I'entreprise plutot qu'a Tacte du depot de bilan. Les auteurs concluent que I'opinion du verificateur est une variable importance dans un modele explicatif de Tissue des depots de bilan, mais qu'elle ne permet pas de predire Tissue du deroulement de la faillite avec davantage d'exactitude qu'un modele mecanique simple. IntroductionThe issuance of an tinqualified opinion by an auditor shortly before the company files for bankruptcy often receives substantial press coverage and generates lawsuits by investors against managemetit and the auditor. Because of such failures,