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This paper aims to investigate whether the amount of local governments’ debt can be predicted by the level of political competition.

The study uses the artificial neural network (ANN) to test whether ANN can “learn” from the observed data and make reliable out-of-sample predictions of the target variable value (i.e. a local government’s debt level) for given values of the predictor variables. An ANN is a non-parametric prediction tool, that is, not susceptible to the common limitations of regression-based parametric forecasting models, e.g. multi-collinearity and latent non-linear relations.

The study finds that “political competition” is a useful predictor of a local government’s debt level. Moreover, a positive relationship between political competition and debt level is indicated, i.e. increases in political competition typically leads to increases in a local government’s level of debt.

The study contributes to public sector reporting literature by investigating whether public debt levels can be predicted on the basis of political competition while discounting factors such as “political ideology” and “fragmentation”. The findings of the study are consistent with the expectations posited by public choice theory and have implications for public sector auditing, policy and reporting standards, particularly in terms of minimising potential political opportunism.

Recent research has confirmed an underlying economic logic that connects each of the three vertices of the “fraud triangle” – a fundamental criminological model of factors driving occupational fraud. It is postulated that in the presence of economic motivation and opportunity (the first two vertices of the fraud triangle), the likelihood of an occupational fraud happening in an organization increases substantially if the overall organization culture is perceived as being slack toward fraud as it helps potential fraudsters in rationalizing their actions (rationalization being the third vertex of the fraud triangle). This paper aims to offer a viable approach for collecting and processing of data to identify and operationalize the key factors underlying employee perception of organization culture toward occupational frauds.

This paper reports and analyses the results of a pilot study conducted using a convenience sampling approach to identify and operationalize the key factors underlying employee perception of organization culture with respect to occupational frauds. Given a very small sample size, a numerical testing technique based on the binomial distribution has been applied to test for significance of the proportion of respondents who agree that a lenient organizational culture toward fraud can create a rationalization for fraud.

The null hypothesis assumed no difference in the population proportions between those who agree and those who disagree with the view that a lenient organizational culture toward fraud can create a rationalization for fraud. Based on the results of the numerical test, the null hypothesis is rejected in favor of the alternative that the population proportion of those who agree with the stated view in fact exceeded the proportion of those who disagreed.

The obvious limitation is the very small size of the sample obtained because of an extremely low rate of response to the survey questionnaires. However, while of course a much bigger data set needs to be collected to develop a generalizable prediction model, the small sample was enough for the purpose of a pilot study.

This paper makes two distinct practical contributions. First, it posits a viable empirical research plan for identifying, collecting and processing the right data to identify and operationalize the key underlying factors that capture an employee’s perception of organizational culture toward fraud as a basis for rationalizing an act of fraud. Second, it demonstrates via a small-scale pilot study that a more broad-based survey can indeed prove to be extremely useful in collating the sort of data that is needed to develop a computational model for predicting the likelihood of occupational fraud in any organization.

This paper provides a viable framework which empirical researchers can follow to test some of the latest advances in the “fraud triangle” theory. It outlines a systematic and focused data collection method via a well-designed questionnaire that is effectively applicable to future surveys that are scaled up to collect data at a nationwide level.

The purpose of this paper is to perform a comparative study of prediction performances of an artificial neutral network (ANN) model against a linear prediction model like a linear discriminant analysis (LDA) with regards to forecasting corporate credit ratings from financial statement data.

The ANN model used in the study is a fully connected back‐propagation model with three layers of neurons. The paper uses a comparative approach whereby two prediction models – one based on ANN and the other based on LDA are developed using identically partitioned data set.

The study found that the ANN model comprehensively outperformed the LDA model in both training and test partitions of the data set. While the LDA model may have been hindered by omitted variables; this actually lends further credence to the ANN model showing that the latter is more robust in dealing with missing data.

A possible drawback in the model implementation probably lies in the selection of the various accounting ratios. Perhaps future replications of this study should look more carefully at choosing the ratios after duly addressing the problems of collinearity and duplications more rigorously.

The findings of this study imply that since ANN models can better deal with complex data sets and do not require restraining assumptions like linearity and normality, it may be overall a better approach in corporate credit rating forecasts that uses large financial data sets.

This study brings out the effectiveness of non‐linear pattern learning models as compared to linear ones in forecasts of financial solvency. This goes on to further highlight the practical importance of the new breed of computational tools available to techno‐savvy financial analysts and also to the providers of corporate credit.

Tacit knowledge is gaining importance in the productive capability of many modern firms, yet the conditions under which the ability to share this form of knowledge between individuals or teams are yet to be resolved. Tacit knowledge is embedded in individuals, but is often most productive when combined with other forms of capital assets into firms. Transaction cost economics has been a useful tool in explaining the boundary of the firm, as well as the formation of teams within firms. The extent to which intra-firm teams compete or co-operate is analyzed by examining the network effects between teams in situations where tacit knowledge exists. We examine the costs and benefits that can be expected from “learning” in a multi-team firm and conduct a simulation to demonstrate the effects. Two scenarios are considered: one when there is almost no specialization between teams, and the second when specialization is extreme. We are able to demonstrate that only in cases of very large differences in tacit knowledge between teams is the transfer of such knowledge profitable. Thus the existence of separate silos within firms (i.e., non-networked teams) should not be condemned out of hand.

Each individual investor is different, with different financial goals, levels of risk tolerance and personal preferences. From the point of view of investment management, these characteristics are often defined as objectives and constraints. Objectives can be the type of return being sought, while constraints include factors such as time horizon, how liquid the investor is, any personal tax situation and how risk is handled. It is really a balancing act between risk and return with each investor having unique requirements, as well as a unique financial outlook – essentially a constrained utility maximization objective. To analyze how well a customer fits into a particular investor class, one investment house has even designed a structured questionnaire with about 24 questions that each has to be answered with values from 1 to 5. The questions range from personal background to what the customer expects from an investment. A fuzzy logic system has been designed for the evaluation of the answers to the above questions. The notion of fuzziness with respect to funds allocation is investigated.