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Self‐efficacy has been defined as individuals’ beliefs, thoughts, and feelings about their personal capabilities that affect how they function and, which in turn influence their performance (Bandura 1977). Self beliefs can influence behaviour choices, determine the amount of effort needed and for how long, and encourage thought patterns and emotional behaviours necessary to succeed. It is theorised that students with unrealistic expectations (especially overly optimistic expectations) may have difficulty aligning efforts with desired performance levels and, as a result, perform more poorly. In this study, selfefficacy is operationalised as the difference between actual and predicted examination performance. Prediction errors in the final examination marks (MERR) and prediction error in the overall course grade (GERR) of a second year management accounting course are used as measures of Self‐efficacy. Using regression analysis, the results indicate that the efficacy measures are significant and positively related to course performance. This suggests that students who are more conservative in their expectations of the course results perform better than those who are more optimistic. The findings also showed that student characteristics of previous academic achievements (CGPA) and hours of study per week (HRWK) explained more that 40 per cent of the variations in the self‐efficacy measures. The higher a student’s CGPA, the more conservative or cautious he is in the prediction of his final course performance. The more pessimistic a student is of his final course performance, the lower the number of hours he estimates for studying the course.

The link between confidence and economic decisions has been widely covered in the economic literature, yet it is still an unexplored field in tourism. The purpose of this paper is to address this gap, and investigate benefits in forecast accuracy that can be achieved by combining the UNWTO Tourism Confidence Index (TCI) with statistical forecasts.

Research is conducted in a real-life setting, using UNWTO unique data sets of tourism indicators. UNWTO TCI is pooled with statistical forecasts using three distinct approaches. Forecasts efficiency is assessed in terms of accuracy gains and capability to predict turning points in alternative scenarios, including one of the hardest crises the tourism sector ever experienced.

Results suggest that the TCI provides meaningful indications about the sign of future growth in international tourist arrivals, and point to an improvement of forecast accuracy, when the index is used in combination with statistical forecasts. Still, accuracy gains vary greatly across regions and can hardly be generalised. Findings provide meaningful directions to tourism practitioners on the use opportunity cost to produce short-term forecasts using both approaches.

Empirical evidence suggests that a confidence index should not be collected as input to improve their forecasts. It remains a valuable instrument to supplement official statistics, over which it has the advantage of being more frequently compiled and more rapidly accessible. It is also of particular importance to predict changes in the business climate and capture turning points in a timely fashion, which makes it an extremely valuable input for operational and strategic decisions.

The use of sentiment indexes as input to forecasting is an unexplored field in the tourism literature.

We investigate auditor objectivity as it relates to engagement quality reviews by examining whether engagement quality reviewers (EQRs) exhibit lower levels of objectivity when they have administrative, economic, or social ties with the audit engagement partner. Motivated reasoning theory suggests that EQRs with ties to the engagement partner will reach less conservative conclusions and be more willing to accept an engagement partner's decision relative to reviewers who have no connections with the engagement partner. We conduct an experiment where EQRs must review a decision by an engagement partner related to a contingent liability.

Results suggest that engagement quality reviews are an effective mechanism for reducing the effects of engagement partner biases to accept client-favored accounting choices. Participants with ties to the engagement partner (i.e., from the same office) and without ties (i.e., from the national office) both challenged the decision of the engagement partner and recommended disclosure of a contingent liability, which client management opposed. We also find an interaction of ties with the engagement partner and the probability of the contingent liability. National office EQRs were less likely to decide that disclosure was necessary than were local office partners when the probability of the contingent liability was low. With regard to the need to recognize a liability, EQRs with and without ties to the engagement partner concurred with the decision of the engagement partner.

This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches.

The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the expected rate of return based on distribution.

The results show that using time series prediction methods has a significant effect on improving investment decisions and the performance of the investments.

In this study, in contrast to previous studies, the alteration in the Markowitz model started with the investment expected rate of return. For this purpose, instead of considering the distribution of returns and determining the expected returns, time series prediction methods were used to calculate the future return of each asset. Then, the results of different time series methods replaced the expected returns in the Markowitz model. Finally, the overall performance of the method, as well as the performance of each of the prediction methods used, was examined in relation to nine stock market indices.

The random walk forecast of exchange rate serves as a standard benchmark for forecast comparison. The purpose of this paper is to assess whether this benchmark is unbiased and directionally accurate under symmetric loss. The focus is on the random walk forecasts of the dollar/euro for 1999‐2007 and the dollar/pound for 1971‐2007.

A forecasting framework to generate the one‐ to four‐quarter‐ahead random walk forecasts at varying lead times is designed. This allows to compare forecast accuracy at different lead times and forecast horizons. Using standard evaluation methods, this paper further evaluates these forecasts in terms of unbiasedness and directional accuracy.

The paper shows that forecast accuracy improves with a reduction in the lead time but deteriorates with an increase in the forecast horizon. More importantly, the random walk forecasts are unbiased and accurately predict directional change under symmetric loss and thus are of value to a user who assigns similar cost to incorrect upward and downward move predictions in the exchange rates.

The one‐ to four‐quarter‐ahead random walk forecasts evaluated here are for averages of daily figures and not for the (end‐of‐quarter) rates in 3‐, 6‐, 9‐ and 12‐months. Thus, the framework is of value to a market participant who is interested in forecasting quarterly average rates rather than the end‐of‐quarter rates.

The exchange rate forecasting framework presented in this paper allows the evaluation of the random walk forecasts in terms of directional accuracy which (to the best of knowledge) has not been done before.

This study evaluates the Federal Reserve (Fed) initial and final forecasts of the unemployment rate for 1983Q1-2018Q4. The Fed initial forecasts in a typical quarter are made in the first month (or immediately after), and the final forecasts are made in the third month of the quarter. The analysis also includes the private forecasts, which are made close to the end of the second month of the quarter.

In evaluating the multi-period forecasts, the study tests for systematic bias, directional accuracy, symmetric loss, equal forecast accuracy, encompassing and orthogonality. For every test equation, it employs the Newey–West procedure in order to obtain the standard errors corrected for both heteroscedasticity and inherent serial correlation.

Both Fed and private forecasts beat the naïve benchmark and predict directional change under symmetric loss. Fed final forecasts are more accurate than initial forecasts, meaning that predictive accuracy improves as more information becomes available. The private and Fed final forecasts contain distinct predictive information, but the latter produces significantly lower mean squared errors. The results are mixed when the study compares the private with the Fed initial forecasts. Additional results indicate that Fed (private) forecast errors are (are not) orthogonal to changes in consumer expectations about future unemployment. As such, consumer expectations can potentially help improve the accuracy of private forecasts.

Unlike many other studies, this study focuses on the unemployment rate, since it is an important indicator of the social cost of business cycles, and thus its forecasts are of special interest to policymakers, politicians and social scientists. Accurate unemployment rate forecasts, in particular, are essential for policymakers to design an optimal macroeconomic policy.

This study is concerned with evaluating the Federal Reserve forecasts of light motor vehicle sales. The goal is to assess accuracy gains from using consumer vehicle-buying attitudes and expectations about future business conditions derived from the long-running Michigan Surveys of Consumers.

Simplicity is a core principle in forecasting, and the literature provides plentiful evidence that combining forecasts from different methods and models reduces out-of-sample forecast errors if the methods and models are valid. As such, the authors construct a simple vector autoregressive (VAR) model that incorporates consumer vehicle-buying attitudes and expectations about future business conditions. Comparable forecasts of vehicle sales from this model are then combined with the Federal Reserve forecasts to assess accuracy gains.

The findings for 1994–2016 indicate that the Federal Reserve and VAR forecasts contain distinct and useful predictive information, and the combination of the two forecasts shows reductions in forecast errors that are more significant at longer horizons. The authors thus conclude that there are accuracy gains from using consumer survey responses.

This is the first study that is concerned with evaluating the Federal Reserve forecasts of vehicle sales and examines whether there are accuracy gains from using consumer vehicle-buying attitudes and expectations.

The purpose of this paper is to investigate the effect of goals on: auditors' inventory write‐off judgments; the conformity of their judgments (i.e. the degree of consistency between these judgments and the judgments they perceive other auditors will make); and the calibration of their judgments (i.e. the extent to which these perceived judgments agree with actual judgments of the other auditors).

An experiment was conducted in which 92 auditors are assigned either an accuracy goal, a goal to get along with the client, or a combined accuracy and get along goal (i.e. both goals), and are asked to make an inventory write‐off judgment.

Consistent with expectations, auditors with accuracy goals are more likely to recommend a write‐off of inventory than auditors in the other goal conditions; and auditors with both goals are more likely to recommend a write‐off than those with the get along goal. Also, while auditors' judgments are well calibrated, mixed evidence of conformity is found.

Goals may be a tool which audit regulators and practitioners could use to enhance audit effectiveness. In addition, the interactive audit environment may contribute to auditors' well calibrated judgments but judgment conformity may require more (such as accountability or incentives) than knowledge of other auditors' judgments.

This is the first paper to examine the impact of explicit and competing goals on the calibration and conformity of auditors' judgments.

This paper aims to extend the research into company financial forecasts by modelling naïve earnings forecasts derived from normalised historic accounting data disclosed during Australian initial public offerings (IPOs). It seeks to investigate naïve forecast errors and compare them against their management forecast counterparts. It also seeks to investigate determinants of differential error behaviour.

IPOs were sampled and their prospectus forecasts, historic financial data and subsequent actual financial performance were analysed. Directional and absolute forecast error behaviour was analysed using univariate and multivariate techniques.

Systematic factors associated with error behaviour were observed across the management forecasts and the naïve forecasts, the most notable being audit quality. In certain circumstances, the naïve forecasts performed at least as well as management forecasts. In particular, forecast interval was an important discriminator for accuracy, with the superiority of management forecasts only observed for shorter forecast intervals.

The results imply a level of “disclosure management” regarding company IPO forecasts and normalised historic accounting data, with forecast overestimation and error size more extreme in the absence of higher quality third‐party monitoring services via the audit process. The results also raise questions regarding the serviceability of normalised historic financial information disclosed in prospectuses, in that many of those data do not appear to enhance the forecasting process, particularly when accompanied by published management forecasts and shorter forecast intervals.

This study aims to systemically evaluate morphological printing errors between computer-aided design (CAD) and reference models fabricated using two different three-dimensional printing (3DP) technologies with hard and soft materials.

The reference models were designed to ensure simpler and more accurate measurements than those obtained from actual kidney simulators. Three reference models, i.e. cube, dumbbell and simplified kidney, were manufactured using photopolymer jetting (PolyJet) with soft and hard materials and multi-jet printing (MJP) with hard materials. Each reference model was repeatably measured five times using digital calipers for each length. These values were compared with those obtained using CAD.

The results demonstrate that the cube models with the hard material of MJP and hard and soft materials of PolyJet were smaller (p = 0.022, 0.015 and 0.057, respectively). The dumbbell model with the hard material of MJP was smaller (p = 0.029) and that with the soft material of PolyJet was larger (p = 0.020). However, the dumbbell with the hard material of PolyJet generated low errors (p = 0.065). Finally, the simplified kidney models with the hard material of MJP and soft materials of PolyJet were smaller (p = 0.093 and 0.021) and that with the hard material of PolyJet was opposite to the former models (p = 0.043).

This study, to the best of authors’ knowledge, is the first to determine the accuracy between CAD and reference models fabricated using two different 3DP technologies with multi-materials. Thus, it serves references for surgical applications as simulators and guides that require accuracy.