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Financial analysts' forecasts serve as a proxy for market earnings expectations, and research provides mixed evidence of the relation between financial analysts' expertise and forecast accuracy. The judgment and decision-making (J/DM) literature suggests that those with more expertise will not perform better when tasks exhibit either extremely high or extremely low complexity. Expertise is expected to contribute to superior performance for tasks between these two extremes. Using archival data, this research examines the effect of analysts' expertise on forecasting performance by taking into consideration the forecasting task's complexity. Results indicate that expertise is not an explanatory factor for forecast accuracy when the forecasting task's complexity is extremely high or low. However, when task complexity falls between these two extremes, expertise is a significant explanatory variable of forecast accuracy. Both results are consistent with our expectations.

The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.

The authors examine the impact of analysts’ prior epidemic experience on forecast accuracy by comparing the changes from the pre-COVID-19 period (calendar year 2019) to the post-COVID period extending up to March 2023 across HRE versus non-HRE analysts. The authors consider a full sample (194,980) and a sub-sample (136,836) approach to distinguish “Recent” forecasts from “All” forecasts (including revisions).

The study's findings reveal that forecast accuracy for HRE analysts is significantly higher than that for non-HRE analysts during COVID-19. Specifically, forecast errors significantly decrease by 0.6% and 0.15% for the “Recent” and “All” forecast samples, respectively. This finding suggests that analysts’ prior epidemic experience leads to an enhanced ability to assess the uncertainty around the epidemic, thereby translating to higher forecast accuracy.

The finding that the expertise developed through an experience of following high-risk firms in the past enhances analysts’ performance during the pandemic sheds light on a key differentiator that partially explains the systematic difference in performance across analysts. The authors also show that industry experience alone is not useful in improving forecast accuracy during a pandemic – prior experience of tracking firms during epidemics adds incremental accuracy to analysts’ forecasts during pandemics such as COVID-19.

The study findings should prompt macroeconomic policymakers at the national level, such as the central banks of countries, to include past epidemic experiences as a key determinant when forecasting the economic outlook and making policy-related decisions. Moreover, practitioners and advisory firms can improve the earning prediction models by placing more weight on pandemic-adjusted forecasts made by analysts with past epidemic experience.

The uncertainty induced by the COVID-19 pandemic increases uncertainty in global financial markets. Under such circumstances, the importance of analysts’ role as information intermediaries gains even more importance. This raises the question of what determines analysts’ forecast accuracy during the COVID-19 pandemic. Building upon prior literature on the role of analyst experience in shaping analysts’ forecasts, the authors examine whether experience in tracking firms exposed to prior epidemics allows analysts to forecast more accurately during COVID-19. The authors find that analysts who have experience in forecasting for firms with high exposure to epidemics (H1N1, Zika, Ebola, and SARS) exhibit higher accuracy than analysts who lack such experience. Further, this effect of experience on forecast accuracy is more pronounced while forecasting for firms with higher exposure to the risk of COVID-19 and for firms with a poor ex-ante informational environment.

This paper investigates whether board gender diversity and the strength of auditing and reporting standards are associated with analysts' forecast accuracy and whether the strength of auditing and reporting standards moderates the association between board gender diversity and analysts' forecast accuracy.

The sample covers 24,086 firm-year observations from 37 countries from 2009 to 2018. The data were obtained from various sources: earnings forecast data from the Institutional Brokers' Estimate System (IBES) database; board gender diversity and financial data from Thomson Reuters Fundamentals; and country-level data from World Economic Forum database. The authors measure board gender diversity using four proxies namely, the proportion of women directors on the board, a dummy variable for board with at least one women director, BLAU measurement corresponds to the proportion of group females and males using the formula adopted from the Hirschman-Herfindahl index (Hirschman, 1964) and the proportion of the number of women executives over the total number of directors. The study also uses a series of specification tests using alternative measures for each variable and controlling the global financial crisis and endogeneity issue.

Firms with higher board gender diversity have higher analysts' forecast accuracy. Compared to countries with weak auditing and reporting standards, the authors find firms in countries with strong auditing and reporting standards have more accurate forecasts. Further, the positive relationship between the board gender diversity and analysts' forecast accuracy is weaker for firms in countries with strong auditing and reporting standards, as compared to firms in countries with weak auditing and reporting standards.

This study found new evidence on the effect of women directorships on analyst forecasts and this relationship varies between levels of the strength of auditing and reporting standards, which was not addressed in prior studies.

This study highlights the importance of strengthening the policy on getting more women on board and the continuous efforts to enhance the strength of auditing and reporting standards of a country as valuable strategies to enhance the quality of analyst forecasts.

This is the first study that employs the international dataset to examine the moderating effect of the strength of auditing and reporting standards on the relationship between board gender diversity and analysts' forecast accuracy.

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.

The purpose of this paper is to identify herding behaviour on financial markets and measure the herding behaviour impact on the accuracy of analysts' earnings forecasts.

Two alternative measures of herding behaviour, on analysts' earnings forecasts are proposed. The first measure identifies herding as the tendency of analysts to forecast near the consensus. The second measure identifies herding as the tendency of analysts to follow the most accurate forecaster. This paper employs the method of The Generalised Method of Moments in order to relax any possible biases.

In both measures employed, a positive and significant relation is found between the accuracy of analysts' earnings forecasts and herding behaviour. According to the first measure analysts exhibit herding behaviour by forecasting close to the consensus estimates. According the second herding measure, it is found that analysts tend to herd towards the best forecaster at the time. Finally, it is concluded that the accuracy of analysts' forecasts increases as herding increases.

The present study triggers concerns for further research in the modelling of analysts' forecasting behaviour.

This paper proposes that a measure based on human biases is the best way to estimate and predict the analysts' earnings forecast future accuracy.

The purpose of this study is to examine the impact of unidentifiable individual differences among financial analysts on the cross section of their earnings forecast accuracy.

The paper employs the concept of analyst fixed effects to control for unidentifiable individual differences. Various psychological factors, such as decision style and personality traits, are documented to impact individuals' decision making. However, analysts' individual differences in such psychological factors are not captured by identifiable personal attributes employed in finance literature, such as years of experience. The methodology used addresses this issue and presents a more comprehensive study of analyst forecast accuracy.

The paper documents that unidentifiable analyst‐specific effects are significant, and that controlling for them improves model fitting and changes the explanatory power of some of the traditionally used independent variables in the literature. The paper confirms that the analyst's firm‐specific experience, the intensity of following that a firm receives, and the forecast horizon are all significantly and consistently related to forecast accuracy. However, it is found that analyst general experience and coverage complexity lose explanatory power when individual differences are controlled for. Analyst general experience is not monotonically associated with better accuracy and that analysts only benefit from increased general experience during the early to middle stages of their career. Finally, when analysts' individual differences are controlled for, the boldness of a forecast revision is not associated with the improvement of accuracy.

The documented non‐monotonic relationship between analyst general experience and forecast accuracy is explained as a result of U‐shaped self‐efficacy, decision style and career concerns. This observation is not necessarily in line with the theory of analyst seniority and reputation‐related performance.

The methodology used addresses the issue of analysts' individual differences in psychological factors and presents a more comprehensive study of analyst forecast accuracy.

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.

This study aims to examine the effects of product market competition (PMC) on analysts’ earnings forecast attributes, particularly forecast accuracy and dispersion. The authors also investigate whether investor protection moderates the relationship between PMC and forecast attributes.

The sample covers 49,578 firm-year observations from 38 countries. This study uses an ordinary least squares regression, a Heckman two-stage regression and an instrumental two-stage least squares regression.

This study finds that PMC is associated with higher forecast accuracy and lower dispersion. The results also show that investor protection enhances the effect of PMC on forecast accuracy and dispersion. These findings imply that countries with strong investor protection have a better information environment, as exhibited by the stronger relationship between PMC and analysts’ forecast properties.

The findings highlight the importance of strong governance mechanisms in both the country and industry environments. Policymakers, including government agencies and financial regulators, can leverage these insights to formulate regulations that promote competition, ensure investor protection and facilitate informed investment decisions.

This study advances our understanding of how PMC affects analysts’ earnings forecast attributes. In addition, it pioneers evidence of the moderating role of investor protection in the relationship between PMC and forecast attributes.

We highlight two mechanisms of limited attention for expert information intermediaries, i.e., analysts, and the effects of such limited attention on the market price discovery process. We approach analysts' limited attention from the perspective of day-to-day arrival of information and processing of tasks. We examine the attention-limiting role of competing tasks (number of earnings announcements and forecasts for portfolio firms) and distracting events (number of earnings announcements for non-portfolio firms) in analysts' forecast accuracy and the effects of such, on the subsequent price discovery process. Our results show that competing tasks worsen analysts' forecast accuracy, and competing task induced limited attention delays the market price adjustment process. On the other hand, distracting events can improve analysts' forecast accuracy and accelerate market price adjustments when such events relate to analysts' portfolio firms through industry memberships.

The purpose of this paper is to examine the impact of management’s choice of forecast precision on the subsequent dispersion and accuracy of analysts’ earnings forecasts.

Using a sample of 3,584 yearly management earnings per share (EPS) forecasts and 10,287 quarterly management EPS forecasts made during the period of 2002-2007 and collected from the First Call database, the authors controlled for factors previously found to impact analysts’ forecast accuracy and dispersion and investigate the link between management forecast precision and attributes of the analysts’ forecasts.

Results provide empirical evidence that managements’ disclosure precision has a statistically significant impact on both the dispersion and the accuracy of subsequent analysts’ forecasts. It was found that the dispersion in analysts’ forecasts is negatively related to the management forecast precision. In other words, a precise management forecast is associated with a smaller dispersion in the subsequent analysts’ forecasts. Evidence consistent with accuracy in subsequent analysts’ forecasts being positively associated with the precision in the management forecast was also found. When the present analysis focuses on range forecasts provided by management, it was found that lower precision (a larger range) is associated with a larger dispersion among analysts and larger forecast errors.

Evidence suggests a consistency in inferences across both annual and quarterly earnings forecasts by management. Accordingly, recent calls to eliminate earnings guidance through short-term quarterly management forecasts may have failed to consider the linkage between the attributes (precision) of those forecasts and the dispersion and accuracy in subsequent analysts’ forecasts.

This study contributes to the literature on both management earnings forecasts and analysts’ earnings forecasts. The results assist in policy deliberations related to calls to eliminate short-term management earnings guidance.