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PurposeThe purpose of this paper is to examine the relevance of asset composition of a firm (tangible versus intangible properties), when evaluating its financial health. The paper argues that relevance of any asset is a function of how effectively it is used.Design/methodology/approachThe paper uses two distinctive samples: a sample of traditional firms holding primarily traditional physical assets and a sample of technology service firms holding primarily intangible assets and examines the ability of intangible assets to surrogate as financial health signals.FindingsThe results show that when evaluating firms with significant intangible assets, using information about intangible assets to improve financial health evaluation. However, fundamental financial variables continue to be important in signaling financial health, regardless of asset composition.Practical implicationsThe results highlight the importance of both objectively‐measured and reported fundamental financial information and subjectively measured intangible asset values. The results would help managers and markets in using greater caution when evaluating firms with intangible assets.Originality/valueUnlike prior studies, this paper uses both fundamental financial variables and surrogates for intangible asset values in the model. The paper contributes by highlight the importance and limitations of intangible asset values.

This study reexamines the accounting profession's response to opportunities and incentives given it during three unique periods in its history to foster reliable accounting, reporting and auditing practices. By profession, we mean the auditors of publicly held companies as represented by the American Institute of Accountants and its predecessor, the American Association of Public Accountants (AAPA). We use two models of professionalism, the Functionalist and the Conflict models, to interpret the profession's response to these events. We find that both self interest and the public interest may have motivated many of the actions taken. These motivations are not, however, mutually exclusive and both may be used to interpret the same behavior.

A number of the reports by academicians and practitioners in the United States have called for significant change in accounting education and an enhanced role for accounting history in curricula and research. However, the survey results reported in this paper suggest that achieving wider acceptance of accounting history presents some perplexing problems. Doctoral faculty, especially assistant professors, report less interest in accounting history than non-doctoral faculty. Although a majority of academicians consider accounting history research to be acceptable for promotion, tenure and hiring decisions and a valuable aid to teaching, practitioners, students, doctoral faculty strongly believe that it is of less value than mainstream empirical research in accounting. Most academicians perceive that research in accounting history is not as methodologically rigorous as other branches of accounting research.

ABSTRACTThis study proposes the use of a modified genetic algorithm (MGA), a global search technique, as a training method to improve generalizability and to identify relevant inputs in a neural network (NN) model. Generalizability refers to the NN model's ability to perform well on exemplars (observations) that were not used during training (out‐of‐sample); improved generalizability enhances NN's acceptability as a valid decision‐support tool. The MGA improves generalizability by setting unnecessary weights (or connections) to zero and by eliminating these weights. Because the eliminated weights have no further impact on the training (in‐sample or out‐of‐sample data), the relevant variables can be identified from the model. By eliminating unnecessary weights, the MGA is able to search and find a parsimonious model that generalizes well. Unlike the traditional NN, the MGA identifies the model variables that contribute to an outcome, helping decision makers to rationalize output and accept results with greater confidence. The study uses real‐life data to demonstrate the use of MGA.

ABSTRACTThis study compares the performance of three artificial neural network (ANN) approaches—backpropagalion, categorical learning, and probabilistic neural network—as classification tools to assist and support auditor's judgment about a client's continued financial viability into the future (going concern status). ANN performance is compared on the basis of overall error rates and estimated relative costs of misclassificaticn (incorrectly classifying an insolvent firm as solvent versus classifying a solvent firm as insolvent). When only the overall error rate is considered, the probabilistic neural network is the most reliable in classification, followed by backpropagation and categorical learning network. When the estimated relative costs of misclassification are considered, the categorical learning network is the least costly, followed by backpropagation and probabilistic neural network.