Terry J. Ward

An Analysis of the Usefulness of Debt Defaults and Going Concern Opinions in Bankruptcy Risk Assessment

This study extends the research of Hopwood et al. (1994) and Mutchler et al. (1997) by empirically investigating the relationships between loan defaults, violation of loan covenants, going-concern opinions, and bankruptcy in bankruptcy prediction models. One objective of this study is to empirically test the ability of loan defaults/accommodations and loan covenant violations to assess the risk of bankruptcy. Another objective of this study is to investigate the impact of failing to control for these two distress events on results from tests of the usefulness of going-concern opinions in assessing bankruptcy risk. Results suggest that loan default/accommodation and loan covenant violation are both significant explanatory variables of bankruptcy at the time of the last annual report before the event. While a going-concern opinion variable appears to significantly explain bankruptcy, it is not significant when included in a model with loan default/accommodation and covenant violation variables. Consequently, our results suggest that researchers should include both loan default/accommodation and covenant violation as control variables when using bankruptcy to test the usefulness of going-concern opinions.

A note on selecting a response measure for financial distress

Since 1966, researchers have examined financial distress prediction models to determine the usefulness of accounting information to lenders. These researchers primarily used legal bankruptcy as the response variable for economic financial distress, or included legal bankruptcy with other events in dichotomous prediction models. However, theoretical models of financial distress normally define financial distress as an economic event, the inability to pay debts when due (insolvency). This study uses a loan default/accommodation response variable as a proxy for the inability to pay debts when due. The purpose of this note is to empirically test whether or not using the inability of a firm to pay debts when due, loan default/accommodation, as a response measure produces different results than using legal bankruptcy as the response measure. The studys empirical results show that legal bankruptcy and loan default/accommodation financial distress prediction models produce different statistical results, thus suggesting that the responses measure different constructs. A loan default/accommodation model also fits the data better than a bankrupt model. Our results suggest that a loan default/accommodation response may be a more appropriate measure to determine which accounting information is most useful to lenders in evaluating a firms credit risk. Copyright Blackwell Publishers Ltd 1997.

Theory of perpetual management accounting innovation lag in hierarchical organizations

Abstract Many researchers claim that management accounting innovations lag technological innovations, thus resulting in outdated management accounting systems that lead to suboptimal decision making. This manuscript examines the causes of management accounting innovation lag in organizations and develops a theory of perpetual accounting lag. Perpetual accounting lag theory is derived from the organizational failure framework and markets and hierarchies theory. The theory asserts that operation of an internal labor market within a hierarchical organization inhibits management accounting innovation.

Supporting ordinal four-state classification decisions using neural networks

Many accounting and finance problems require ordinal multi-state classification decisions, (e.g., control risk, bond rating, financial distress, etc.), yet few decision support systems are available to aid decision makers in such tasks. In this study, we develop a Neural Network based decision support system (NN-DSS) to classify firms in four ordinal states of financial condition namely healthy, dividend reduction, debt default and bankrupt. The classification results of the NN-DSS model are compared with those of a Naïve model, a Multiple Discriminant Analysis (MDA) model, and an Ordinal Logistic Regression (OLGR) model. Four different evaluation criteria are used to compare the models, namely, simple classification accuracy, distance-weighted classification accuracy, expected cost of misclassification (ECM) and ranked probability score. Our study shows that NN-DSS models perform significantly better than the Naïve, MDA, and OLGR models on the ECM criteria, and provide better results than MDA and OLGR on other criteria, although not always significantly better. The effect of the proportion of firms of each state in the training set is also studied. A balanced training set leads to more uniform (less skewed) classification across all four states, whereas an unbalanced training set biases the classification results in favor of the state with the largest number of observations.

An Empirical Analysis of Thomas's Financial Accounting Allocation Fallacy Theory in a Financial Distress Context

Abstract Thomas (1969, 1974 and 1975) theoretically attacked the practice of incorporating major accounting allocations across time such as depreciation and deferred taxes in financial accounting. Instead, he advocated using accrual-based funds statements as alternatives to an income statement (preferably a net-quick-assets funds statement). This paper reports the results of analyses of Thomass assertions by using the predictive ability criterion, and the ordinal four-state financial distress methodology developed by Ward (1994). Results generally support Thomass assertions. A net-quick-assets operating flow and an operating flow variable adjusted for depreciation and amortisation and deferred tax allocations are both normally stronger predictors of financial distress than a net income variable. However, contrary to Thomass theory, the change in inventory, a non-monetary item, appears to be an important predictor of financial distress one year before distress.

Continuous reporting benefits in the private debt capital market

The study is an experiment, administered over the Internet, measuring the effect that continuous reporting has on a companys ability to secure private debt capital. Specifically, we test whether commercial loan officers would be more willing to increase the probablity of loan acceptance to a mid-sized company operating in a continuous reporting environment than they would a company that operates in a traditional reporting environment. We find that high risk companies providing financial information to the lender on a daily basis have a higher probability of loan acceptance than do companies providing financial information to the lender on a quarterly basis. We did not find any results for low risk companies, suggesting the potential benefits of continuous reporting might not accrue to those type companies. The results were robust for both new and existing banking relationship scenarios. We did not find any results for the interest rate variable. The results of this study have significant implications for companies determined to be high risk. Commercial loans are the life-support for many companies, and failure to secure a line-of-credit could have devastating consequences for these high-risk companies.

Investigation of Artificial Neural Networks for Classifying Levels of Financial Distress of Firms: The Case of an Unbalanced Training Sample

Accurateprediction of financial distress of firms is crucial to bank lending officers, financial analysts, and stockholders as all of them have a vested interest in monitoring the firms’ financial performance. Most of the previous studies concerning predicting financial distress were performed for a dichotomous state such as nonbankrupt versus bankrupt or no going concern opinion versus going concern opinion. Many studies used well-balanced samples. Much less than one-half of firms become distressed and firms generally progress through different levels of financial distress before bankruptcy. Therefore, this study investigates the usefulness of artificial neural networks in classifying several levels of distress for unbalanced but somewhat realistic training samples. The chapter also compares the classification ability of neural networks and logistic regression through extensive computer simulation of several experiments. Results from these experiments indicate that analysis with two cascaded neural networks produce the best classification results. One network separates healthy from distressed firms only, the other network classifies those firms identified as distressed into one of three distressed states.

A comparison of the ability of neural networks and logit regression models to predict levels of financial distress

In this study we compared the classification accuracy rates of neural networks to those from ordinal logit models for a multi-state response variable. The results indicate that with the multi-state response variable, neural networks produce higher overall classification rates than ordinal logit models, but do not more accurately classify distressed firms. As a result, we can not clearly state that neural networks are superior to regression when predicting more than one level of financial distress.

Artificial Neural Networks in Predicting a Dichotomous Level of Financial Distress for Uneven Training and Testing Samples

To adequately perform their duties, bank lending officers, financial analysts, and auditors must accurately assess companies’ financial performance. Stockholders also have a financial incentive to monitor companies’ financial performance. Accurately predicting financial distress is an important part of the assessment and monitoring process. For decades, these individuals used traditional statistical techniques such as regression analysis, logit regression (LR) models, or discriminant analysis to try to predict which companies are likely to be healthy and which ones will go bankrupt. In the last several years, one can observe a growing interest in the use of relatively new data mining tools such as artificial neural networks (NNs) for the tasks of prediction, classification, and clustering.