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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.

Using data from securities analysts, who are awarded status by the third-party organization Institutional Investor magazine, we examine the emergence of competition and articulate a model of competitive response among actors aware of the importance of status and some of the dimensions on which it may be gained. We predict analysts’ initiating or ceasing coverage of stocks in response to other analysts initiating coverage on stocks they cover. We find that competition can emerge because of status seeking rather than as a response to own capabilities or market needs, with compelling, and potentially negative, market implications for overt status seeking.

The bankruptcy of Enron in December 2001 marked the beginning of broad awareness that American corporations had left behind the strategy of expanding through diversification that was the hallmark of the 1950s through the early 1980s. CEOs now made it job one to meet the earnings projections of securities analysts, such that by the year 2000 they were, in record numbers, “restating earnings” – admitting that they had cooked the books. Accounting shenanigans were the tip of the iceberg, and what lay under the water was a new approach to running the corporation to produce numbers that analysts and institutional investors would like. Three groups that stood to benefit from the new strategy spun it to investors as in the interest of all. Managers of hostile takeover firms defined their business as setting firms on the path to performing for shareholders. Institutional investors defined earnings management, rather than acquisitions management, as increasing shareholder value and focused management attention on earnings by popularizing stock options. Securities analysts hawked their own profit projections as the reigning metric of corporate performance, and favored easy-to-analyze single-industry firms through “buy” recommendations. These three groups changed the incentives executives faced, making accounting shenanigans in the pursuit of earnings management widely popular and enriching institutional investors, analysts, and executives in the process. Regulatory changes to end malfeasance have made it marginally more difficult to perform illegal accounting practices, but they have not changed the core corporate strategy that has emerged since the early 1980s. The changes illuminate the rise of groups of business professionals in the power structure, for it was not investors but different groups of business professionals who won the day. The changes illuminate, as well, the role of the social construction of interest in power relations among groups – it was by convincing executives and shareholders that a new corporate strategy was in their own interest, which these business professionals succeeded.

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.

This article suggests that both producers and analysts are strategic about categorization. Producers use categorization to maintain a balance of differentiation and legitimacy, whereas analysts seek to influence categorization and clarify boundaries. Ideas are explored for software producers and Gartner, the preeminent high-technology analyst. Findings show evidence of strategic categorization. Producers move to proximate market categories in response to competition. Gartner reports on large categories and those that receive investment and stops reporting on categories that have fuzzy boundaries. Compared to analysts, producers may be more influential in category creation than previous research has acknowledged.

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.

Krull (2004) finds that US multinational corporations (MNCs) increase amounts designated as permanently reinvested earnings (PRE) to maximize reported after-tax earnings and meet earnings targets. We extend this research by examining the relationship between executive equity compensation and the opportunistic use of PRE by US MNCs, and the market reaction to earnings management using PRE designations. Firms use equity compensation to incentivize executives to strive for maximum shareholder wealth. One unintended consequence is that executives may engage in earnings management activities to increase their equity compensation. In this study, we examine whether the equity incentives of management are associated with an increased use of PRE. We predict and find strong evidence that the changes in PRE are positively associated with the portion of top managers' compensation that is tied to stock performance. In addition, we find this relationship to be strongest for firms that meet or beat forecasts, but only with the use of PRE to inflate income, suggesting that equity compensation incentivizes managers to opportunistically use PRE, especially to meet analyst forecasts.

Further, we provide evidence that investors react negatively to beating analysts' forecasts with the use of PRE, suggesting that investors find this behavior opportunistic and not fully convincing. This chapter makes an important contribution to what we know about the joint effects of tax policy, generally accepted accounting principles, and incentive compensation on the earnings reporting process.

In this study, I investigate analysts’ ability to process public information for investors by examining price reactions to a sample of analysts’ recommendation revisions issued shortly after quarterly earnings announcements. I find that these recommendation revisions are used by investors to reassess the valuation implications of announced earnings. Confirmatory (contradictory) recommendation revisions that have the same (opposite) sign as prior earnings surprises can cause investors to revise their beliefs about the valuation implications of announced earnings upward (downward) and thus cause price reactions that are positively (negatively) associated with prior earnings surprises. In addition, I find that as the information complexity of earnings announcements gets higher, these recommendation revisions play a more important role in helping investors understand the valuation implications of announced earnings. Finally, I find that analysts’ ability to interpret the valuation implications of announced earnings for investors has remained at a similar level since the adoption of Regulation Fair Disclosure. Overall, this study provides additional evidence on how analysts help improve corporate information environment.

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.

One question of interest to several different groups is whether capital markets value environmental information and, if so, to what extent this information is incorporated into security valuation models. This paper addresses these questions by reporting on a survey of financial analysts and other influential members of the financial community with respect to their knowledge of various types of environmental information and their use of this information in security analysis. The results, while exploratory in nature, indicate a surprising lack of knowledge among the respondents concerning various organizations and reporting initiatives that are well known in environmental circles. A pervasive theme running through the results suggests that while most analysts do not explicitly incorporate environmental variables into their evaluation models, for those that do, their focus is on downside risk rather than upside potential. The results suggest that the reluctance to make widespread use of environmental information is due, at least partly, to concern with the reliability of the available information. This suggests that more work needs to be done to communicate relevant and reliable environmental performance information to the investment community.