Bankruptcy Events Research Articles

Neural network prediction analysis: The bankruptcy case

In this paper we evaluate the prediction capability of various neural network models. The models examined in this study differ on the following parameters: data span, learning technique and number of iterations. The neural net prediction capabilities are also compared to results obtained by classical discriminant analysis models. The specific case evaluated is bankruptcy prediction. The common assumption of all bankruptcy prediction models is that fundamental economic factors and the characteristics of a firm are reflected in its financial statements. Therefore, using analytic tools and data from the firm's financial reports, one can evaluate and predict its future financial status. Since the number of bankrupt firms is limited, we used examples (financial statements) from various periods preceding the bankruptcy event. Although the financial statements from the bankruptcy period convey more information, financial statements form distinct periods always improved the models. The prediction capability of the models is improved by using enhanced learning techniques. However, if the enhancement learning technique is too ‘strong’, the model becomes too specific for the training data set and thus loses its prediction capabilities.

STATISTICAL DECOMPOSITION ANALYSIS OF FINANCIAL STATEMENTS AND PREDICTION OF BOND RATING CHANGES

In recent years, a sizeable amount of research in finance and accounting has been devoted to the issue of bond rating and bond rating changes. A major thrust of these research efforts was to develop and test some prediction‐based models using mainly financial ratios and their trends. This paper tests the ability of statistical decomposition analysis of financial statements to predict bond rating changes. The results show that the decomposition analysis almost does not beat the a priori probability model and is no better than multiple discriminant analysis using simple financial ratios. One important piece of information for participants in debt markets is the assessment of the relative risk associated with a particular bond issue, commonly known as bond ratings. These ratings, however, are not usually fixed for the life of the issues. From time to time, the rating agencies review their ratings of the outstanding bond issues and make changes to these ratings (either upward or downward) when needed. Over the years, researchers have attempted to develop and test some prediction based models in order to predict bond ratings or bond rating changes. These prediction models have employed some variables that are assumed to reflect the rating agency decision‐making activities. Although the rating process is complicated and based mainly on judgmental considerations, Hawkins, Brown and Campbell (1983, p. 95) reported that the academic research strongly suggests that a reliable estimate of a potential bond rating or rating change can be determined by a few key financial ratios. Information theory decomposition measures have received in recent years considerable attention as a potential tool for predicting corporate events, namely corporate bankruptcy (e.g., Lev 1970; Moyer 1977; Walker, Stowe and Moriarity 1979; Booth 1983). The underlying proposition in these studies is that corporate failure, as an event, is expected to be preceded by significant changes in the company's assets and liabilities structure. Although the event of bond rating changes is different from the bankruptcy event in terms of consequences, one can still propose that a bond rating change, as a corporate event, is also expected to be preceded by some significant changes in the company's assets and liabilities structure. Therefore, the decomposition analysis may have a predictive ability in the case of bond rating changes. The purpose of this paper is to empirically test and compare the classification and predictive accuracy of the decomposition analysis with the performance of a multiple discriminant model that uses financial ratios and their trends in the context of bond rating changes.