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The purpose of this paper is to develop and test the scale of auditors’ awareness of the profession’s reputation for independence, defined as the degree to which auditors recognise the importance of the reputation for independence and acknowledge the impact of their judgements and decisions on that reputation, and to provide preliminary evidence of an association between auditors’ awareness of the profession’s reputation and auditors’ ethical judgement.

A seven-item scale was developed to measure auditors’ awareness of the profession’s reputation for independence, and an auditing case was used to measure auditors’ ethical judgement. A survey questionnaire of practising auditors working in auditing firms in Indonesia provides data for testing the validity and reliability of the new scale and proposed hypothesis.

The findings show that the scale is unidimensional and has satisfied reliability and validity. Moreover, the preliminary evidence of a positive association between the new scale and auditors’ ethical judgement is provided.

Further studies should test the validity and reliability of the scale of awareness of the profession’s reputation for independence with larger data and in different settings. Investigation of the antecedent factors of auditors’ awareness of the profession’s reputation for independence is suggested.

This paper develops a new measure, namely, the awareness of the profession’s reputation for independence. Preliminary evidence to establish an association between that awareness and auditor ethical judgement is provided.

Considers the modelling of dynamic systems using biased regression and spectral methods. Provides evidence on the power of transfer function modelling for unravelling the empirical connection between endogenous and exogenous (control) variables in both regression type and spectral input‐output systems. The Multiple Input Transfer Function Noise Model – of specific value when the input variables are collinear – has previously been used to demonstrate the connection between macroeconomic forces and stock market pricing on a thin security market. Compares the adequacy of representative time and frequency domain algorithms for modelling observed data series. The estimations are done with the combined Transfer Function and Cartesian ARIMA Search algorithm of Östermark and Höglund and the CAPM/APM programs of Östermark.

In the paper we design a super genetic hybrid algorithm (SuperGHA), an integrated optimization system for simultaneous parametric search and nonlinear optimization. The parametric search machine is implemented as a genetic superstructure, producing tentative parameter vectors that control the ultimate optimization process. The family of parameter vectors evolves through ordinary genetic operators aimed at producing the best possible parameterization for the underlying optimization problem. In comparison to traditional genetic algorithms, the integrated superstructure involves a twofold ordering of the population of parameter vectors. The first sorting key is provided by the objective function of the optimization problem at issue. The second key is given by the total mesh time absorbed by the parametric setting. In consequence, SuperGHA is geared at solving an optimization problem, using the best feasible parameterization in terms of optimality and time absorbance. The algorithm combines features from classical nonlinear optimization methodology and evolutionary computation utilizing a powerful accelerator technique. The constrained problem can be cast into multiple representations, supporting the integration of different mathematical programming environments. We show by extensive Monte Carlo simulations that SuperGHA extracts suitable parameter vectors for fast solution of complicated nonlinear programming problems.

The performance of Aoki’s state space algorithm and the Cartesian ARIMA search algorithm (CARlMA) of Östermark and Höglund is compared. The analysis is carried out on a set of stock prices on the Helsinki (Finland) and Stockholm (Sweden) Stock Exchanges. Demonstrates that the Finnish and Swedish stock markets differ in predictability of stock prices. With Finnish stock data, Aoki’s state space algorithm outperforms the subset of MAPE minimizing forecasts. In contrast, with Swedish stock data, ARIMA‐models of a fairly simple structure outperform Aoki’s algorithm. The stock markets are seen to differ in complexity of time series models as well as in predictability of individual asset prices.

The approach described is based on conceptual analysis and practical experience on large‐scale financial planning models. In the authors' view, improved ergonomics in modelling can be achieved by visualising not only the MCDM stage of the model but the whole model structure. Three visualisations are constructed that cover managerial trade‐offs, environmental uncertainty and resource requirements. The results presented are based on real‐world complex financial models in a large commercial bank. It is suggested that the quality of the model/user interface does not seem to depend so much on the interactiveness of the tools used, but on the comprehensive illustrations that guide managers to essential discussions.

The study focuses on the problem of parametric interdependence in fuzzy linear programs. The relevance of the research issue stems from the fact that in real world problems, the mathematically best parameter combination with nonexistent interdependence is not necessarily feasible under more restricting conditions. One way to control the parameter combinations entering the LP is to assume some mathematical relations between the fuzzy numbers of the model. The study suggests one approach for extracting combinations of parameters in the fuzzy LP while explicitly recognizing their interdependence (the position vector method). With a strictly convex or concave membership function uniqueness of parameter combinations is secured.

Provides evidence on the power of transfer function noise modelling in explaining the empirical connection between endogenous and exogenous (control) variables in linear regression type input‐output systems. The multiple input transfer function noise model – of specific value when the input variables are collinear – is used to demonstrate the connection between macroeconomic forces and stock market pricing on a thin security market. Shows that the transfer function approach provides new evidence partly in conflict with previous results obtained by ordinary least squares methodology. Previous empirical evidence suggests that money supply, inflation, the level of industrial production and the psychological impact of the general index of the Stockholm Stock Exchange affects Finnish stock pricing. The problem of selecting relevant economic state variables is tackled by regressing each of the five factor time series obtained from testing the arbitrage pricing theory (see Östermark, circa 1989) on the set of tentative state variables. The economic state variables are significant explanators of stock pricing, both at the market and at the individual asset level. Only nine individual stocks are tested. Comprehensive testing of all individual stocks is left for future research.

To solve the multi‐period portfolio management problem under transactions costs.

We apply a recently designed super genetic hybrid algorithm (SuperGHA) – an integrated optimisation system for simultaneous parametric search and non‐linear optimisation – to a recursive portfolio management decision support system (SHAREX). The parametric search machine is implemented as a genetic superstructure, producing tentative parameter vectors that control the ultimate optimisation process.

SHAREX seems to outperform the buy and hold‐strategy on the Finnish stock market. The potential of a technical portfolio system is best exploitable under favorable market conditions.

A number of robust engines for matrix algebra, mathematical programming and numerical calculus have been integrated with SuperGHA. The engines expand its scope as a general‐purpose algorithm for mathematical programming.

Presents for the first time empirical evidence on the forecasting performance of multi‐layer neural nets in modelling multiple‐input vector time series processes. Compares the results produced by the neural net with those obtained by a robust VARMAX‐algorithm and a multiple‐input state space algorithm for vector‐valued time series processes. The neural net and the VARMAX‐algorithm were programmed in the C‐language and the state space algorithm was programmed in FORTRAN.

To discuss a new parallel algorithmic platform (minlp_machine) for complex mixed‐integer non‐linear programming (MINLP) problems.

The platform combines features from classical non‐linear optimization methodology with novel innovations in computational techniques. The system constructs discrete search zones around noninteger discrete‐valued variables at local solutions, which simplifies the local optimization problems and reduces the search process significantly. In complicated problems fast feasibility restoration may be achieved through concentrated Hessians. The system is programmed in strict ANSI C and can be run either stand alone or as a support library for other programs. File I/O is designed to recognize possible usage in both single and parallel processor environments. The system has been tested on Alpha, Sun and Linux mainframes and parallel IBM and Cray XT4 supercomputer environments. The constrained problem can, for example, be solved through a sequence of first order Taylor approximations of the non‐linear constraints and feasibility restoration utilizing Hessian information of the Lagrangian of the MINLP problem, or by invoking a nonlinear solver like SQP directly in the branch and bound tree. minlp_machine( ) has been tested as a support library to genetic hybrid algorithm (GHA). The GHA(minlp_machine) platform can be used to accelerate the performance of any linear or non‐linear node solver. The paper introduces a novel multicomputer partitioning of the discrete search space of genuine MINLP‐problems.

The system is successfully tested on a small sample of representative MINLP problems. The paper demonstrates that – through concurrent nonlinear branch and bound search – minlp_machine( ) outperforms some recent competing approaches with respect to the number of nodes in the branch and bound tree. Through parallel processing, the computational complexity of the local optimization problems is reduced considerably, an important aspect for practical applications.

This paper shows that binary‐valued MINLP‐problems will reduce to a vector of ordinary non‐linear programming on a suitably sized mesh. Correspondingly, INLP‐ and ILP‐problems will require no quasi‐Newton steps or simplex iterations on a compatible mesh.