Disclosure of contingent environmental liabilities: some unintended consequences?

Article Abstract:

A study examined the consequences of disclosure of contingent environmental liabilities. Financial managers, bankers and MBA students reviewed the distribution of potential environmental liability disclosed by a fictional company. The users were presented with one of four parameters, namely, minimum, best estimate, maximum or range, while others were given no parameter. Users given different parameters created partly overlapping or nonoverlapping distributions of potential environmental liabilities, which is consistent with anchoring. Findings also revealed that differences in formulated distributions influenced their judgments and decisions. The findings were not explained by the users' perceptions of management credibility and firm risk. This study proved that anchoring can affect the judgments and decisions of financial statement users exposed to alternative disclosures of continent environmental liabilities.

Liability for environmental damages, Financial statements, Disclosure statements (Accounting)

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An approach to statistical inference in cross-sectional models with security abnormal returns as dependent variable

Article Abstract:

Research into abnormal securities' returns and their relationship to corporations' characteristics results in the development of a procedure for analyzing disturbances in normally expected security returns to identify influential cross-sectional regression parameters. The parameters developed do not depend upon estimation or inversion of samples of covariance matrices of the return disturbances for their creation, but are dependent upon a portfolio time-series approach to estimation. This approach is simpler than the traditional three-step cross-sectional regression approach. Portfolio time-series regression is explained as a process of: (1) establishing portfolio weights based upon firm characteristics, and (2) conducting time-series regressions on portfolios containing the firms' underlying securities.

Methods, Economic aspects, Stocks, Securities, Corporations, Economic forecasting, Return on investment, Earnings per share, Rate of return, Statistical hypothesis testing

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A note on optimal sample sizes in compliance tests using a formal Bayesian decision-theoretic approach for finite and infinite populations

Article Abstract:

The establishment of optimal sampling size relative to auditing compliance-testing is examined using the same audit populations used in 1982 in the research performed by Godfrey and Andrews. Optimal sampling solutions are determined for both finite and infinite samples, using a formal (rather than the popularly accepted informal) Bayesian decision-theoretic approach. The research suggests that auditors should use formal, as well as informal, Bayesian methods when determining number of tests to run prior to formulating an opinion as to the company's compliance with its stated internal control procedures.

Planning, Auditing, Bayesian statistical decision theory, Bayesian analysis, Statistical sampling, Sampling (Statistics), Mathematical optimization, Optimization theory

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