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Purpose Mandatory disclosure of a firm’s intellectual capital (IC) is restricted by accounting regulations, leading companies to use voluntary disclosure to inform their stakeholders about their IC. However, voluntary IC disclosure (ICD) is costly and may lead to a leak of knowledge. Consequently, firms should only engage in voluntary ICD if it really reduces information asymmetries and leads to reduced cost of capital or a better reputation. The purpose of this paper is to review, integrate and critically discuss the results of studies examining various effects of voluntary ICD. Design/methodology/approach The authors use a structured literature review approach. Findings The results mainly support the expected positive effects of voluntary ICD on monetary value for disclosing firms, e.g. lower cost of capital, higher firm value or increased analysts’ following. Nevertheless, the studies mainly represent second stage IC research. Research limitations/implications Additional studies concerning effects of voluntary ICD outside capital markets are recommended. Future studies should be based on an improved study design concerning the theoretical underpinning and concept of value relevance, sufficient sample sizes and alternative sources of ICD. Practical implications Due to positive monetary effects, firms should engage in voluntary ICD. Originality/value The paper reviews and integrates the state-of-the-art of empirical research of effects of voluntary ICD. It contributes to and enlarges the debate concerning the value relevance of voluntary ICD with respect to the different stages of IC research.

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -Accounting and decision making rely heavily on forecasts. For several reasons, we should expect ongoing increases in forecasting accuracy. The purpose of this paper is to test the hypothesis of improved forecasts over time. Design/methodology/approach -The paper analyzes original monthly sales plans and current data for three different car models in six different countries over 15 years and over several product life cycles (PLCs). Forecasting accuracy is calculated as one minus forecasting error. Forecasting error is measured with MAD/MEAN for periods of years or relative deviations per month. The hypothesis of decreasing forecasting errors is tested with the non-parametric Mann/Kendall trend test. Additional interviews with managers were conducted to elicit details of internal forecasting organization and instruments. Findings -The paper finds no evidence of increased forecasting accuracy in general over 15 years or over subsequent PLCs. This seems surprising, given improved statistical methods and software in general, and experience and learning effects of the organization itself. However, there is evidence from the case, that the reason lies in environmental uncertainty and volatility and not in internal factors within the control of the company. Research limitations/implications -Evidence from one case study is limited in its external validity. Future studies should analyze the forecasts of more companies, more industries and different forecasting objects, the latter including consumer, industrial goods and services. In the absence of further research, the results seem to negate the common assumption, that companies are generally able to make accurate forecasts, including those for accounting purposes. This hypothesis is clearly confuted. Practical implications -The paper describes a methodology for companies to analyze their own forecasting accuracy and to identify possible reasons for a lack of accuracy, or basic approaches to increasing it. Originality/value -Most studies on forecasting accuracy rely on interviews and questionnaires, entailing bias that is difficult to control. Few studies analyze archival data in order to measure forecasting accuracy; so that o...

Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization. The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum. And the initial phase of the breeding process starts with parent selection. The selection utilized in a GA is effective on the convergence speed of the algorithm. A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals. Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study. And the use of neural networks aims to produce better offspring during the GA search. The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring. In the breeding process, the first parent is selected based on the fitness value as usual. Then it is the neural network that decides the appropriate mate for the first parent chosen. Hence, the selection mechanism is not solely dependent on the fitness values in this study. The algorithm is tested with seven benchmark functions. It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.