Purpose – The purpose of this paper is to study gender diversity on the board of directors and the relation to risk management and corporate performance as measured by the variability of stock market return. Design/methodology/approach – The sample consists of companies from the RiskMetrics database from 2007 to 2011. This database contains information on corporate board of directors. Financial variables were collected from the Compustat database and CRSP database for the years 2005-2011. The authors then measure the effect of gender diversity on corporate performance in terms of firm risk, using the model by Cheng (2008) which measures the variability of stock market return. Findings – The study shows that more gender diversity on the board of directors impacts firm risk by contributing to lower variability of stock market return. The higher the percentage of female directors on the board, the lower the variability of corporate performance. Originality/value – The research design and findings assist in providing additional evidence about the role of women in corporate leadership positions and the association with corporate performance. The approach combines Cheng's (2008) model of stock market variability with the impact of gender diversity on the board of directors.

An auditor gives a going concern uncertainty opinion when the client company is at risk of failure or exhibits other signs of distress that threaten its ability to continue as a going concern. The decision to issue a going concern opinion is an unstructured task that requires the use of the auditor's judgment. In cases where judgment is required, the auditor may benefit from the use of statistical analysis or other forms of decision models to support the final decision. This study uses the generalized reduced gmhent (GRG2) optimizer for neural network learning, a backpropagation neural network, and a logit model to predict which firms would receive audit reports reflecting a going concern uncertainty modification. The GRG2 optimizer has previously been used as a more efficient optimizer for solving business problems. The neural network model formulated using GRG2 has the highest prediction accuracy of 95 percent. It performs best when tested with a small number of variables on a group of data sets, each containing 70 observations. While the logit procedure fails to converge when using our eight variable model, the GRG2 based neural network analysis provides consistent results using either eight or four variable models. The GRG2 based neural network is proposed as a robust alternative model for auditors to support their assessment of going concern uncertainty affecting the client company. Subject Areas: Auditing, Decision Processes, and Decision Support Systems.*The authors are thankful to Professor Ming Hung for his suggestions on the use of the GRG2 optimizer, and to the anonymous reviewers for their useful comments and suggestions.

The purpose of this study is to evaluate a hybrid system as a decision support model to assist with the auditor's going-concern assessment. The going-concern assessment is often an unstructured decision that involves the use of both qualitative and quantitative information. An expert system that predicts the going-concern decision has been developed in consultation with partners at three of the Big Five accounting firms. This system is combined with a statistical model that predicts bankruptcy, as a component of the auditor's decision, to form a hybrid system. The hybrid system, because it combines the use of quantitative and qualitative information, has the potential for better prediction accuracy than either the expert system or statistical model predicting separately. In addition, testing of the system provides some insight into the characteristics of firms that experience problems, but do not necessarily receive a goingconcern modification. Further investigation into those firms that have problems could reveal factors that may be incorporated into decision support systems for auditors, in order to improve accuracy and reliability of these decision tools.

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This paper examines how earnings quality affects the investment decisions of Chinese companies who employ non-Big 4 auditors. We measure earnings quality through the companies’ use of discretionary accruals to manage earnings, and by the quality of the companies’ auditors. We then seek to determine whether the quality of the earnings and the quality of the audit relate to overconfidence in internal decision making and lead to excess investment. We use two models for our study, adapted from the model used by McNichols and Stubben (2008). The first model measures the impact of earnings management on investment. Our second model employs a logistic regression model to measure the significant variables in companies that over-invest. We find that more important clients have significantly higher investment than less important clients, and that discretionary accruals are significant indicators of over-investment. Less important clients are more conservative in their investments, although they have more investment opportunities. We also observe that the proportion of over-investment drops for clients, regardless of their importance, whose auditors have a long tenure.

Purpose The purpose of this paper is to examine whether companies with female executives and directors are less likely to be involved in financial reporting fraud litigation. Design/methodology/approach The authors build a data set comprised of companies from the Stanford Securities Class Action Clearinghouse database that were involved in fraud litigation along with a control set of companies listed on the Compustat database for the time period 2007-2013. The authors use a logistic regression model to determine the likelihood of fraud when there is at least one woman in an executive position or on the board of directors. Findings The authors find that the presence of at least one female leader decreases the likelihood that the company will be involved in litigation for financial reporting fraud. The results are robust after controlling for sample selection bias by using a propensity score matched sample. Practical implications The findings add to the literature which indicates that women tend to be more risk averse and are more committed to ethics policies. The study also supports previous research that indicates large firms with inflated market value are more likely to be subject to fraud litigation. Originality/value The study combines the literature on the characteristics of women in leadership positions with the study of fraud litigation. The authors find evidence that the presence of either female executives or female directors lowers financial reporting fraud risk.

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