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This paper aims to contribute the existing finance literature by examining whether the ratings of Morningstar in Australia provide useful information for an investor by way of investigating the efficiency of domestic Australian equity funds that received a rating as at November 2005.

This paper proposes the application of a simulation approach to stochastic data envelopment analysis (SDEA) based on Excel/@RISK, which was first proposed by Premachandra et al. in 1998. The analysis provides a variety of informative statistical information about the stochastic properties of the efficiency figure. Efficiency is measured by looking at fund performance, fund cost, fund risk exposure and manager ability over the period December 1990‐November 2005.

The introduction to portfolio performance evaluation of a new tool for evaluating fund performance (namely, the efficiency rating) is recommended. From a sample of Australian domestic equity managed funds we find that evidence exists to suggest that efficient funds are likely to receive an upgrade in rating in the medium to long term.

The benefit of the present paper for investors and fund managers is the improved efficiency of managed funds in terms of expense and risk.

The present paper further contributes to the literature by reinforcing the importance of SDEA as a tool for measuring the efficiency of decision‐making units within investment fund markets.

We identify a novel concept of discretionary idiosyncratic volatility proxied by the idiosyncratic volatility component not related to the non-systematic industry volatility as a source of agency problems that have implications for firms’ cash holdings and their investment decisions. We find that firms with low discretionary idiosyncratic volatility, which likely captures discretionary effort and risk-taking by managers, have smaller cash reserves. Moreover, while high discretionary idiosyncratic volatility firms spend cash internally (internal capital building), low discretionary idiosyncratic volatility firms use it for external acquisitions, consistent with the “quiet life” hypothesis. Our findings thus indicate a need for reinforcement of existing regulations and corporate laws to control for agency costs, which could in turn reduce firm risk and the probability of financial meltdown at the aggregate level.

The purpose of this paper is to present an integrated algorithm composed of data envelopment analysis (DEA), corrected ordinary least squares (COLS) and principal component analysis (PCA) to estimate efficiency scores of electricity distribution units.

Several DEA and COLS models are prescribed and their results are verified and validated by the algorithm. To calculate efficiency scores, three standard internal consistency conditions between DEA and COLS results are checked by the algorithm. If these conditions are satisfied, DEA is chosen as the superior model because it could be used for optimization as well. Otherwise, the geometric mean of DEA and COLS model is used as the final efficiency scores.

The algorithm of this paper may be easily applied to decision‐making units because of its robustness (combined DEA‐COLS input and output) and validity gained through PCA.

The integrated approach has several unique features which are: verification and validation mechanism by PCA, consideration of internal consistency conditions between DEA and COLS and consolidation of DEA and COLS for improved ranking given consistency conditions are violated.

The purpose of this paper is to improve the information quality of bankruptcy prediction models proposed in the literature by building prediction intervals around the point estimates generated by these models and to determine if the use of the prediction intervals in conjunction with the point estimated yields an improvement in predictive accuracy over traditional models. The authors calculated the point estimates and prediction intervals for a sample of firms from 1991 to 2008. The point estimates and prediction intervals were used in concert to classify firms as bankrupt or non-bankrupt. The accuracy of the tested technique was compared to that of a traditional bankruptcy prediction model. The results indicate that the use of upper and lower bounds in concert with the point estimates yield an improvement in the predictive ability of bankruptcy prediction models. The improvements in overall prediction accuracy and non-bankrupt firm prediction accuracy are statistically significant at the 0.01 level. The authors present a technique that (1) provides a more complete picture of the firm’s status, (2) is derived from multiple forms of evidence, (3) uses a predictive interval technique that is easily repeated, (4) can be generated in a timely manner, (5) can be applied to other bankruptcy prediction models in the literature, and (6) is statistically significantly more accurate than traditional point estimate techniques. The current research is the first known study to use the combination of point estimates and prediction intervals to in bankruptcy prediction.

Managers often face the problem of making project decisions that involve a large number of interrelated activities – the planning and scheduling of which is project management. These problems often arise in areas such as product development, production planning and control and setting up of production facilities. One important aspect of project management is activity crashing, i.e. reducing activity time by adding more resources such as workers, overtime and so on. It is important to decide the optimal crash plan to complete the project within the desired time period. The usual practice is to use a linear programming (LP) approach. Finds that the solution obtained from LP is not always effective and shows that a goal‐programming approach can be used efficiently in such decision‐making problems.

This paper aims to propose an integration of the analytic hierarchy process (AHP) and data envelopment analysis (DEA) methods in a multiattribute grey relational analysis (GRA) methodology in which the attribute weights are completely unknown and the attribute values take the form of fuzzy numbers.

This research has been organized to proceed along the following steps: computing the grey relational coefficients for alternatives with respect to each attribute using a fuzzy GRA methodology. Grey relational coefficients provide the required (output) data for additive DEA models; computing the priority weights of attributes using the AHP method to impose weight bounds on attribute weights in additive DEA models; computing grey relational grades using a pair of additive DEA models to assess the performance of each alternative from the optimistic and pessimistic perspectives; and combining the optimistic and pessimistic grey relational grades using a compromise grade to assess the overall performance of each alternative.

The proposed approach provides a more reasonable and encompassing measure of performance, based on which the overall ranking position of alternatives is obtained. An illustrated example of a nuclear waste dump site selection is used to highlight the usefulness of the proposed approach.

This research is a step forward to overcome the current shortcomings in the weighting schemes of attributes in a fuzzy multiattribute GRA methodology.

The purpose of this paper is to propose the data envelopment analysis (DEA) model that can be used as binary-valued data. Often the basic DEA models were developed by assuming that all of the data are non-negative. However, there are situations where all data are binary. As an example, the information on many diseases in health care is binary data. The existence of binary data in traditional DEA models may change the behavior of the production possibility set (PPS). This study defines a binary summation operator, expresses the modified principles and introduces the extracted PPS of axioms. Furthermore, this study proposes a binary integer programming of DEA (BIP-DEA) for assessing the efficiency scores to use as an alternate tool in prediction.

In this study, the extracted PPS of modified axioms and the BIP-DEA model for assessing the efficiency score is proposed.

The binary integer model was proposed to eliminate the challenges of the binary-value data in DEA.

The importance of the proposed model for many fields including the health-care industry is that it can predict the occurrence or non-occurrence of the events, using binary data. This model has been applied to evaluate the most important risk factors for stroke disease and mechanical disorders. The targets set by this model can help to diagnose earlier the disease and increase the patients’ chances of recovery.

The purpose of this paper is to investigate whether the quality of the credit process is sensitive to reaching a particular threshold level of non-performing loans (NPLs) and, more importantly, whether higher NPLs ratios could make the monitoring activity ineffective.

The empirical design is composed of two steps: in the first step, the authors introduce a monitoring performance indicator (MPI) of the credit process by combining the non-parametric technique Data Envelopment Analysis with some financial ratios adopted as input and output variables. As second step, the authors apply a threshold panel regression model to a sample of 298 Italian banks, over the time period 2006–2014, and the authors investigate whether the quality of the credit process is sensitive to reaching a particular threshold level of NPLs.

This paper finds that, first, when the NPLs ratio remains below the threshold value estimated endogenously, an increase in the quality of monitoring has a positive impact on the NPLs ratio. Second, if the NPLs ratio exceeds the estimated threshold, the relationship between the NPLs ratio and quality of monitoring assumes a positive value and is statistically significant.

Due to the lack of data, the investigation of NPLs in the Italian industry across loan types combined with the monitoring effort by banks management was not possible. The authors plan to investigate this topic in future studies.

The identification of the threshold has a double operational valence. The first regards the Supervisory Authority, the threshold approach could be used as an early warning in order to introduce active control strategies based on the additional information requested or by on-site inspections. The second implication is highlighted in relation to the individual banks, the monitoring of credit control quality, if objective and comparable, could facilitate the emergence of best practices among banks.

A high NPLs ratio requires greater loan provisions, which reduces capital resources available for lending, and dents bank profitability. Moreover, structural weaknesses on banks’ balance sheets still persist particularly in relation to the inadequate internal governance structures. This means that bank management must able to recognise in advance early warning signals by providing prudent measurement together with an in-depth valuation of loans portfolio.

The originality of the paper is twofold: the authors introduce a new proxy of credit monitoring, called MPI; the authors provide an empirical proof of the Diamond’s (1991) economic intuition: for riskier borrowers, the monitoring activity is an inappropriate instrument depending on the bad reputational quality of borrowers.

The purpose of this paper is to illustrate the application of statistical tools and techniques for quantitative assessment of spiritual capital (SC) based on a questionnaire survey in the organizations which undergo large-scale organizational change projects.

A sample of 65 individuals from three organizations were interviewed. The paper uses the 12 principles of transformation available to spiritual intelligence (referred to as SQ characteristics) to assess SC in a two-phase integrated algorithm of principal component analysis (PCA) and fuzzy clustering.

The paper proposes a two-phase integrated algorithm. In the first phase, PCA is used to reduce the scores of items related to each of SQ characteristics and aggregate them into a single and unique measure. In the second phase, PCA is applied for total SQ quantification. For verification and validation, fuzzy clustering is employed along with PCA to cluster the people in the survey into different classes, which may possess different stocks of SC and rank them based on their level of SQ. The results of PCA are verified and validated by fuzzy clustering revealing the applicability and usefulness of PCA for SC quantification.

The paper is based on individual judgments about their own SQ characteristics hence the results of questionnaire survey may be biased by individual personal characteristics. Future research can apply the proposed algorithm and check for its reliability using other psychometric instruments available in the field.

The paper contributes by filling a gap in the quantitative management tools literature, in which empirical studies on validated multivariate analysis of spirituality have been scarce until now.

The purpose of this paper is to examine whether the presence, expertise, independence, size and meetings of the audit committee (AC) have an effect on corporate insolvency.

The authors use 1,835 firm-year observations for 98 insolvent and 269 solvent UK-listed non-financial firms from 1994 to 2011.

The authors find that corporate insolvency is negatively related to the meetings and independence of the AC but not to AC’s presence and size. The authors also observe that financial expertise on the AC is not related to corporate insolvency. These associations are robust to different specifications, while after controlling for board composition, board size, the number of board meetings, CEO duality, financial and firm characteristics.

The study’s approach has two main limitations: neglect of small and medium private unquoted firms and more regulated corporate governance environment.

The findings lend support to the continual use of the agency theory as an explanation in understanding the role of the analytical lens through which to study the efficacy of the AC in reducing the likelihood of insolvency.

The findings support continued efforts to strengthen boards’ ACs in the wake of high profile insolvencies. The findings will assist regulators and firm management to design appropriate ACs (e.g. independence) and processes (e.g. number of meetings).

The authors provide empirical evidence on the impact of the AC on firm insolvency in the UK context, an important but neglected area in research.