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We evaluate the performance of financial analysts versus naïve models in making long-term earnings forecasts. Long-term earnings forecasts are generally defined as third-, fourth-, and fifth-year earnings forecasts. We find that for the fourth and fifth years, analysts' forecasts are no more accurate than naïve random walk (RW) forecasts or naïve RW with economic growth forecasts. Furthermore, naïve model forecasts contain a large amount of incremental information over analysts' long-term forecasts in explaining future actual earnings. Tests based on subsamples show that the performance of analysts' long-term forecasts declines relative to naïve model forecasts for firms with high past earnings growth and low analyst coverage. Furthermore, a model that combines a naïve benchmark (last year's earnings) with the analyst long-term earnings growth forecast does not perform better than analysts' forecasts or naïve model forecasts. Our findings suggest that analysts' long-term earnings forecasts should be used with caution by researchers and practitioners. Also, when analysts' earnings forecasts are unavailable, naïve model earnings forecasts may be sufficient for measuring long-term earnings expectations.

The aim of this paper is to analyze the impact of corporate governance (focused on some key mechanisms as board size, board independence, managerial ownership, institutional ownership, and chief executive officer duality) on financial analysts’ behavior in US. Results from panel data analysis for 294 US listed firms observed from 2007 to 2014 show that several attributes of the board of directors and audit committee have no effects on the number of analysts who are following the firm and the properties of analysts’ earnings forecasts. Findings also suggest that firms with independent and large boards and blockholders ownership benefit of more analyst following. In addition, it is proven that analysts’ earnings forecasts are optimistic and more accurate for companies where blockholder ownership, either by managers or external entities have larger quoted spreads but of lower quality for the ones which have greater independent board members and institutional investor’s holding.

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.

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.

Financial analysts act as crucial conduits of information between firms and stakeholders. However, comparatively little is known about how these information intermediaries evaluate the believability and importance of corporate disclosures. We argue that a firm’s level of managerial discretion, or latitude of executive action, acts as a cue for financial analysts, which helps them interpret and respond to voluntary management earnings forecasts. Our study provides strong, robust evidence that financial analysts find management forecasts significantly less believable in low-discretion than in high-discretion environments, and therefore tend to be much less responsive to these forecasts. We also show that managerial discretion is especially impactful on analysts’ responses in those circumstances where analysts are typically most uncertain about how to interpret management forecasts.

The authors examine the relationship between credit default swaps (CDS) initiation and managers’ earnings forecast choices with different corporate governance structures. The authors expect that corporate governance plays a significant role in managers’ disclosure behavior as well as CDS initiation. The findings suggest that CDS initiation and managers’ earnings forecast behavior are positively associated. Firms with a strong monitoring mechanism issue a higher number of earnings forecasts and also issue forecasts more frequently when there is a traded CDS contract in the market. Additionally, the results suggest that managers issue more accurate earnings forecasts. Overall, these findings imply that the role of managers is important to mitigate the information asymmetry between individual and institutional investors when there is a new financial instrument because the development of the regulations and market rules for these instruments takes a longer time.

Findings in the literature indicate leading financial analysts attract high levels of market attention and provide more accurate earnings forecasts prior to becoming all-star analysts. Furthermore, these analysts significantly impact the investment decisions of other market participants and thus the market price of assets. Therefore, this study examines the information role of leading financial analysts and identifies two significant conclusions. First, the positive outcomes of these analyst leaders are more informative and attract more followers. Second, informational herding by followers of these analysts is not as naïve as suggested in previous studies, as followers who smartly use information from analyst leaders tend to perform better. We also find that analysts who practice smart learning by studying and selectively employing analyst-leader decisions achieve better career outcomes.

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.

To investigate the nature of investment expertise and factors affecting the information processing and performance of investment experts, this paper hypothesizes normative characteristics of investment expertise and compares such characteristics with actual characteristics documented in prior literature on the investment expert. Based on collective evidence from these sources, a model of investment expertise is proposed.

Results support the existence of investment expertise in (1) the nature of knowledge, (2) problem solving and information search, and (3) performance. A variety of factors that could influence the information processing and performance of the investment expert, including personal, cognitive, and contextual elements, are also discussed in the paper and included in the proposed model of investment expertise.

This chapter examines the use of mathematical programming to remove systematic bias from demand forecasts. A debiasing methodology is developed and applied to demand data from an actual service operation. The accuracy of the proposed methodology is compared to the accuracy of a well-known approach that utilizes ordinary least squares regression. Results indicate that the proposed method outperforms the least squares approach.