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Past research has shown that forecast combination typically improves demand forecast accuracy even when only two component forecasts are used; however, systematic bias in the component forecasts can reduce the effectiveness of combination. This study proposes a methodology for combining demand forecasts that are biased. Data from an actual manufacturing shop are used to develop the methodology and compare its accuracy with the accuracy of the standard approach of correcting for bias prior to combination. Results indicate that the proposed methodology outperforms the standard approach.

The purpose of this paper is to investigate the impact of audit quality and ownership structure on the degrees of accuracy and bias in earnings forecasts issued in initial public offering (IPO) prospectuses in a frontier market, Bangladesh.

The paper uses both univariate and multivariate tests on the sample of 75 IPOs. The paper employs the tests to see the association between the degree of forecast bias and three corporate governance variables.

The results reveal that the magnitude of earnings forecast bias is significantly explained by issuer, auditor reputation, proportions of capital raised from domestic as well as foreign investors, and whether the IPO firm is a start-up venture. Underwriter prestige, length of the issuing firms' operating history, leverage, whether the firm went public during a stock market boom, and forecast horizon do not appear to be statistically significant in explaining the degree of forecast bias.

Although auditor reputation and the proportion of equity retained by pre-IPO owners have been investigated in several studies on IPO forecast accuracy and/or bias, no study has attributed them to corporate governance as a whole by combining auditor reputation, and ownership categories held by small private investors and foreign portfolio investors.

This study examines the relationship between managerial overconfidence and corporate financing decisions by constructing proxies for managerial overconfidence based on the track records of earnings forecasts in Japanese listed firms. We find that managers have the stable tendency to forecast overly upward earnings compared to actual ones and that their upward bias decreases the probability of issuing equity in the public market by about 4.7 percent per one standard error, which economically has the strongest impact on financing decisions. This tendency is observed when we employ alternative measures for managerial overconfidence and other model specifications. However, in private placements, the choice to offer equity is not always avoided by managers. This implies that managers place private equity with the expectation of the certification effect

This study examines the accuracy and bias of profit forecasts disclosed in prospectuses by New Zealand companies for initial public offerings during the period 1987 to 1994. The results show that profit forecasts in this period are, on average, more accurate titan those disclosed prior to 1987, which were examined in prior studies. However, the results reject the null hypothesis that profit forecasts are accurate. In examining forecast bias, the evidence shows that the forecasts are, on average, somewhat pessimistic, but not sufficiently to reject the hypothesis that profit forecasts are unbiased. Tests of the determinants of error show that larger companies make more accurate forecasts, and forecasts made in the year 1987 are less accurate than in other years. Tests of the determinants of bias show that forecasts made in 1987 are also more optimistic, and that companies with longer trading histories and pessimistic forecasts make less biased forecasts. Forecast period and industry type are not significantly related to error or bias.

The purpose of this paper is to assess whether there is an association between CEO age and analysts forecast properties (particularly forecast accuracy and bias/optimism). CEOs, having the central role in managing firms, can significantly influence the financial and non-financial decisions in an organisation. Furthermore, having been identified as key culprits in past major accounting scandals, it is also important to identify the CEO characteristics that affect financial reporting decisions.

This study adopts the upper echelon theory on the relationship between CEO age and analysts forecast properties. The authors use a sample of 2,730 Australian firm-year observations for the period 2004–2013 drawn from IBES, Connect 4 and SIRCA databases.

The authors find that analyst forecast accuracy increases and bias (optimism) reduces with the CEO age. The authors conclude that earnings and related information provided to analysts improves with the CEO age, which increases the forecast accuracy and reduces bias (optimism). Additional results suggest that the positive (negative) effect of CEO age on forecast accuracy (bias) remains until the CEOs reach the age of their retirement age (65 years). The results remain consistent with a number of sensitivity tests and provide implication for stakeholders such as firms, analysts, auditors, financial statements users and regulators.

The authors demonstrate that the relationship between CEO age and analyst forecast properties is not linear but is, in fact, curvilinear substantiating concerns that CEOs that are much younger or much older do not help increase the quality of the information environment. Consequently, firms hiring CEOs in the right age bracket also benefit by having higher-quality information environment leading possibly to reduce costs such as those relating to debt and/or equity ultimately increasing firm value.

Empirical studies on the association between CEO age and analysts earnings properties in Australia are scarce and this paper contributes to the determinants of the analysts forecast accuracy and bias (optimism) and the CEO age literature.

The purpose of this paper is to show that intentional demand forecast bias can improve warehouse capacity planning and labour efficiency. It presents an empirical methodology to detect and implement forecast bias.

A forecast model integrates historical demand information and expert forecasts to support active bias management. A non-linear relationship between labour productivity and forecast bias is employed to optimise efficiency. The business analytic methods are illustrated by a case study in a consumer electronics warehouse, supplemented by a survey among 30 warehouses.

Results indicate that warehouse management systematically over-forecasts order sizes. The case study shows that optimal bias for picking and loading is 30-70 per cent with efficiency gains of 5-10 per cent, whereas the labour-intensive packing stage does not benefit from bias. The survey results confirm productivity effects of forecast bias.

Warehouse managers can apply the methodology in their own situation if they systematically register demand forecasts, actual order sizes and labour productivity per warehouse stage. Application is illustrated for a single warehouse, and studies for alternative product categories and labour processes are of interest.

Intentional forecast bias can lead to smoother workflows in warehouses and thus result in higher labour efficiency. Required data include historical data on demand forecasts, order sizes and labour productivity. Implementation depends on labour hiring strategies and cost structures.

Operational data support evidence-based warehouse labour management. The case study validates earlier conceptual studies based on artificial data.

This paper presents a mathematical programming model to reduce bias for both aggregate demand forecasts and lower echelon forecasts comprising a hierarchical forecasting system. Demand data from an actual service operation are used to illustrate the model and compare its accuracy with a standard approach for hierarchical forecasting. Results show that the proposed methodology outperforms the standard approach.

It has been widely documented that investors and analysts underreact to information in past earnings changes, a fundamental performance indicator. The purpose of this paper is to examine whether managers’ voluntary disclosure efficiently incorporates information in past earnings changes, whether analysts recognize and fully anticipate the potential inefficiency in management forecasts and whether managers’ potential forecasting inefficiency entirely results from intentional disclosure strategies or at least partly reflects managers’ unintentional information processing biases.

Archival data were used to empirically test the relation between management earnings forecast errors and past earnings changes.

Results show that managers underreact to past earnings changes when projecting future earnings and analysts recognize, but fail to fully anticipate, the predictable bias associated with past earnings changes in management forecasts. Moreover, analysts appear to underreact more to past earnings changes when management forecasts exhibit greater underestimation of earnings change persistence. Further analyses suggest that the underestimation of earnings change persistence is at least partly attributable to managers’ unintentional information processing bias.

This study contributes to the voluntary disclosure literature by demonstrating the limitation in the informational value of management forecasts. The findings indicate that the effectiveness of voluntary disclosure in mitigating market mispricing is inherently limited by the inefficiency in management forecasts. This study can help market participants to better use management forecasts to form more accurate earnings expectations. Moreover, our evidence suggests a managerial information processing bias with respect to past earnings changes, which may affect managers' operational, investment or financing decisions.

The purpose of this paper is to detect quantitatively the existence of anchoring bias among financial analysts on the Tunisian stock market. Both non-parametric and parametric methods are used.

Two studies have been conducted over the period 2010–2014. A first analysis is non-parametric, based on observations of the sign taking by the surprise of result announcement according to the evolution of earning per share (EPS). A second analysis uses simple and multiple linear regression methods to quantify the anchor bias.

Non-parametric results show that in the majority of cases, the earning per share variations are followed by unexpected earnings surprises of the same direction, which verify the hypothesis of an anchoring bias of financial analysts to the past benefits. Parametric results confirm these first findings by testing different psychological anchors’ variables. Financial analysts are found to remain anchored to the previous benefits and carry out insufficient adjustments following the announcement of the results by the companies. There is also a tendency for an over/under-reaction in changes in forecasts. Analysts’ behavior is asymmetrical depending on the sign of the forecast changes: an over-reaction for positive prediction changes and a negative reaction for negative prediction changes.

The evidence provided in this paper largely validates the assumptions derived from the behavioral theory particularly the lessons learned by Kaestner (2005) and Amir and Ganzach (1998). The authors conclude that financial analysts on the Tunisian stock market suffer from anchoring, optimism, over and under-reaction biases when announcing the earnings.

One component of revenue forecast error has been attributed to the phenomena of consistent underestimation bias due asymmetrical loss. Because underestimation of revenue forecast results in less loss to forecasters than overestimations, there appears to be a bias for forecasters to underestimate revenue forecasts. This paper confirms this hypothesis. Additionally, with the greater usage of national forecasting organizations that provide economic forecasts on which revenue forecasts are based, a secondary source of forecaster bias may be present in many state level forecasts. This hypothesis is supported by the increase in number of states using such organizations and a decrease in the standard deviation of the annual mean percentage state forecast error.