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Thomson financial database reports a monthly consensus measure of analysts’ forecasts in the third week of every month, and firms’ earnings announcement dates are usually different from the last consensus calculation date. Thus, there is a gap between the last consensus calculation date and the earnings announcement date of firms. This study aims to address the question: “Do analysts issue forecasts that are slightly higher than the consensus number to increase the accuracy of their forecasts?”

This study is based on a sample of 91,172 quarterly earnings forecasts of various firms from 1990 to 2007 made between the last consensus calculation date and quarterly earnings announcement date. Descriptive statistics and statistical tests were used to analyze the data.

The findings propose that contrary to expectation, analysts’ forecasts between the last consensus calculation date and earnings announcement date are smaller than the consensus number. Also, the forecasts made between the last consensus and earnings announcement date is not as informative as forecasts made at other times as they could merely reflect the analysts’ herding behavior resulting from their career concerns.

This study provides a link between the literature that studies firms’ meet or beat analysts’ earnings phenomenon and analysts’ forecast decision-making context. This study also provides useful implications for the literature on the information content of analysts’ forecasts.

This study aims to propose a consensus model that considers dynamic trust and the hesitation degree of the expert's evaluation, and the model can provide personalized adjustment advice to inconsistent experts.

The trust degree between experts will be affected by the decision-making environment or the behavior of other experts. Therefore, based on the psychological “similarity-attraction paradigm”, an adjustment method for the trust degree between experts is proposed. In addition, we proposed a method to measure the hesitation degree of the expert's evaluation under the multi-granular probabilistic linguistic environment. Based on the hesitation degree of evaluation and trust degree, a method for determining the importance degree of experts is proposed. In the feedback mechanism, we presented a personalized adjustment mechanism that can provide the personalized adjustment advice for inconsistent experts. The personalized adjustment advice is accepted readily by inconsistent experts and ensures that the collective consensus degree will increase after the adjustment.

The results show that the consensus model in this paper can solve the social network group decision-making problem, in which the trust degree among experts is dynamic changing. An illustrative example demonstrates the feasibility of the proposed model in this paper. Simulation experiments have confirmed the effectiveness of the model in promoting consensus.

The authors presented a novel dynamic trust consensus model based on the expert's hesitation degree and a personalized adjustment mechanism under the multi-granular probabilistic linguistic environment. The model can solve a variety of social network group decision-making problems.

This paper aims to use Australian analysts' forecast data to compare the relative accuracy of consensus and the most recent forecast in the month before the earnings announcement.

Cross‐sectional regression is used on a sample of 4,358 company‐year observations of annual analyst forecasts to examine whether the number of analysts following and the timeliness of an individual analyst's forecast is more strongly associated with the superior forecast measure.

The results suggest that whilst in the late 1980s the most recent forecast was more accurate than the consensus, since the early 1990s the accuracy of the consensus forecast has outperformed the most recent forecast in 15 out of 17 years, and the differences are significant for nine out of 15 years. The forecasting superiority of the consensus can be attributed to the aggregating value of the consensus outweighing the small timing advantage of the most recent forecast over the short forecast horizon examined in this paper.

Given the consistent use of analysts' forecasts as proxies for expected earnings in Australian research, this paper provides insights to what extent the expected level of forecast accuracy is realised and the reasons for the greater accuracy in the superior forecast measure.

The findings confirm market practitioners' views that the consensus forecast is a better measure of the market's earnings expectations.

This paper provides direct evidence of the accuracy of alternative forecast measures and the importance of diversifying idiosyncratic individual error across analyst forecasts.

This paper provides an objective approach based on available market information capable of reducing subjectivity, inherently present in the process of expected loss provisioning under the IFRS 9.

This paper develops the two-step methodology. Calibrating the Credit Default Swap (CDS)-implied default probabilities to the through-the-cycle default frequencies provides average weights of default component in the spread for each forward term. Then, the impairment provisions are calculated for a sample of investment grade and high yield obligors by distilling their pure default-risk term-structures from the respective term-structures of spreads. This research demonstrates how to estimate credit impairment allowances compliant with IFRS 9 framework.

This study finds that for both investment grade and high yield exposures, the weights of default component in the credit spreads always remain inferior to 33%. The research's outcomes contrast with several previous results stating that the default risk premium accounts at least for 40% of CDS spreads. The proposed methodology is applied to calculate IFRS 9 compliant provisions for a sample of investment grade and high yield obligors.

Many issuers are not covered by individual Bloomberg valuation curves. However, the way to overcome this limitation is proposed.

The proposed approach offers a clue for a better alignment of accounting practices, financial regulation and credit risk management, using expected loss metrics across diverse silos inside organizations. It encourages adopting the proposed methodology, illustrating its application to a set of bond exposures.

No previous research addresses impairment provisioning employing Bloomberg valuation curves. The study fills this gap.

Cost transparency is of central importance to reach a consensus between supply chain partners. The purpose of this paper is to contribute to the instrument of cost analysis which supports the link between buyers and suppliers.

Based on a detailed literature review in the area of cost analysis and purchasing, intelligent decision support systems for cost estimation are identified. Subsequently, expert interviews are conducted to determine the application possibilities for managers. The application potential is derived from the synthesis of motivation, identified applications and challenges in the industry. Management recommendations are to be derived by bringing together scientific and practical approaches in the industry.

On the one hand, the results of this study show that machine learning (ML) is a complex technology that poses many challenges for cost and purchasing managers. On the other hand, ML methods, especially in combination with expert knowledge and other analytical methods, offer immense added value for cost analysis in purchasing.

Digital transformation allows to facilitate the cost calculation process in purchasing decisions. In this context, the application of ML approaches has gained increased attention. While such approaches can lead to high cost reductions on the side of both suppliers and buyers, an intelligent cost analysis is very demanding.

This chapter reviews research on math disabilities (MD) from two different points of view: Italian and American. Our goal is to gain consensus on identifying the cognitive deficits that underlie problems associated with MD as well as to provide an overview of some of the instructional approaches to remediate these deficits. The review outlines similarities and differences in the research perspectives between the two countries. Although the results show some consensus on the identification of MD and the cognitive mechanisms associated with this deficit (e.g., working memory), some differences remain between the two research perspectives (e.g., incidence of MD).

The purpose of this paper is to describe how the voluntary living wage (LW) in the UK is set. It examines how this calculation relates to contemporary approaches to setting wage floors, both in relation to their goal of supporting adequate living standards and in relation to the place of wage floors in the labour mark.

The paper examines how compulsory and voluntary wage floors are being determined, in the UK and in particular the role of public consensus in contributing to the calculation and adoption of a LW. It then reflects on the future sustainability of a system of wage floors in which the concept of the LW plays a significant role.

The central finding is that widespread support for wages delivering socially acceptable minimum living standards has transformed the context in which low pay is being addressed in the UK. The LW idea has stimulated more decisive efforts to do so; however, if a compulsory version of a LW were to reach a level shown to be harming jobs, this could seriously undermine such efforts. Moreover, the extent to which adequate wages are compatible with high employment levels can also be influenced by state support for households, especially tax credits and Universal Credit.

The paper clarifies how the setting of the UK LW contributes to objectives related both to living standards and to labour markets, and critically addresses some key issues raised.

Accelerated share repurchases (ASRs) represent an important recent innovation in repurchase methods. Although executives often mention signaling undervaluation as a motivation for ASRs, managing earnings per share (EPS) has been argued as a key alternative motivation in the financial press. This paper aims to investigate whether ASRs are driven by managerial opportunism (i.e. managing EPS) or managerial optimism (i.e. signaling undervaluation) and whether stock market participants see through these motives.

The sample consists of 293 ASRs conducted between 2004 and 2011. Firms suspected of using ASRs to manage EPS (EPS-suspect firms) were identified by examining actual reported EPS, as-if EPS (i.e. EPS that would have been reported in the absence of an ASR), and analysts’ consensus EPS forecasts. A logistic regression of EPS-suspect versus non-EPS-suspect ASR transactions was performed. Analysts’ reactions to ASR announcements and investors’ reactions to post-ASR earnings announcements were examined. Changes in post-ASR operating performance were also analyzed.

Twenty-nine per cent of ASR firms (EPS-suspect firms) would have missed the consensus EPS forecasts had they not implemented the repurchase. Managerial incentives – securing bonuses and maintaining reputation by avoiding EPS misses – appear to lie behind this opportunistic use of ASRs. Upward revision observed in analysts’ EPS forecasts upon the announcement of ASRs is short-lived, indirectly facilitating firms’ use of ASRs to meet or beat consensus forecasts. Investors, however, are not fooled by managers’ use of ASRs as an earnings management device. Unlike EPS-suspect firms, non-EPS-suspect firms exhibit positive abnormal operating performance during the post-ASR period, suggesting that these firms use ASRs as a signaling device rather than as an earnings management device.

ASRs can be used by managers to signal better future performance to investors. However, managers who intend to do so should carefully consider the timing of an ASR. Initiating an ASR when the company is facing the risk of missing analysts’ EPS forecasts may be interpreted as the ASR being motivated by EPS management concerns rather than signaling, diminishing the credibility of a positive signal intended to be conveyed through the ASR. Further, when considering payout policy and executive compensation decisions, corporate boards need to be cognizant of managers’ incentives for undertaking ASRs. The use of ASRs opportunistically to boost EPS is prevalent, and this action is followed by poor performance.

A number of novel results are documented using tests that are methodologically distinct from those used in related previous research. Notably, this is the first study to distinguish between EPS-suspect and non-EPS-suspect ASR firms and examine the determinants as well as consequences of using ASRs as an earnings management versus signaling device. One out of every four ASR firms are EPS-suspects. Analysts react to ASR announcements by only temporarily increasing their short-term EPS forecasts. Investors see through managers’ use of ASRs as an earnings management device. While ASRs are prone to managerial opportunism, a large number of firms use ASRs to communicate favorable information about their future operating performance to investors.

The purpose of this paper is to report on research concerning financial analysts’ activity surrounding profit warnings issued by listed companies in the four largest European stock exchanges (France, Germany, the Netherlands and the UK). The authors address three aspects of analysts’ forecasts: ex-post accuracy of forecasts, earnings forecast revisions, and consensus forecast dispersion. The goal of the analysis is to study the differences between financial analysts’ behavior within different regulatory settings, namely common law vs civil law countries.

The sample is composed of 1,330 profit warnings issued by listed European firms during the period 2000-2010. The authors apply event study methodology and OLS regressions to highlight the impact of the legal information environment on analysts’ reactions.

The empirical analysis reveals that analyst activity depends on each country’s legal context factors, such as the legal information environment of the firm and the index of investor protection. Accordingly, the authors show that both a richer legal information environment and stronger country-level investor protection substantially improve analyst accuracy around profit warnings.

The sample is only composed on firms from four European countries owing to a lack of firms from other European countries that disclosed PW during the period 2000-2010. It would be pertinent to conduct future research dealing with an international sample from different continents.

The paper contributes to a deeper understanding of analysts’ reactions to profit warnings. The findings can influence firms’ reporting practices and lead to future regulation policies.

This work is the first to examine the relationship between profit warning releases and the behavior of financial analysts in a pan-European context where there are different institutional levels of investor protection.