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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.

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.

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.

In this study, we investigate whether higher institutional ownership is related to better internal controls and whether better internal control is associated with a higher quality of transparency.

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 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.

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.

This study examines short selling as one external determinant of corporate tax avoidance. Prior research suggests that short sellers have information advantages over retail investors, and high short-interest levels are a bearish signal of targeted stock prices. As a result, when short-interest levels are high, managers have been shown to take actions to minimize the negative effect of high short interest on firms’ stock prices. Tax-avoidance activities may convey a signal of bad news (i.e., high stock price crash risk). We predict that, when short-interest levels are high, managers possess incentives to reduce firm tax avoidance in order to reduce the associated stock price crash risk. Consistent with this prediction, we find that short interest is negatively associated with subsequent tax-avoidance levels. This effect is incremental to other factors identified by prior research. We conclude that short selling significantly constrains corporate tax avoidance.

Although prior research documents that analysts sometimes herd their forecasts, very few studies investigate how investors’ judgments are influenced by their perceptions of the likelihood of analyst herding. I conduct an experimental study to investigate the conditions under which investors’ assessments of uncertainty about future earnings are influenced by their perceptions of the likelihood of analyst herding. As expected, and consistent with motivated reasoning, the results show that the temporal order of analyst forecasts influences investors’ estimates of the likelihood of analyst herding and investors’ uncertainty judgments when analyst forecasts are preference-inconsistent but not when analyst forecasts are preference-consistent. This study provides a potential explanation for the mixed findings of prior research in regard to investors’ reactions to the likelihood of analyst herding. In addition, this study extends research on investors’ credulity by providing evidence that motivated reasoning and skepticism may serve as a mechanism that contributes to that credulity.

Following the Supreme Court’s 1988 decision in Basic, securities class plaintiffs can invoke the “rebuttable presumption of reliance on public, material misrepresentations regarding securities traded in an efficient market” [the “fraud-on-the-market” doctrine] to prove classwide reliance. Although this requires plaintiffs to prove that the security traded in an informationally efficient market throughout the class period, Basic did not identify what constituted adequate proof of efficiency for reliance purposes.

Market efficiency cannot be presumed without proof because even large publicly traded stocks do not always trade in efficient markets, as documented in the economic literature that has grown significantly since Basic. For instance, during the recent global financial crisis, lack of liquidity limited arbitrage (the mechanism that renders markets efficient) and led to significant price distortions in many asset markets. Yet, lower courts following Basic have frequently granted class certification based on a mechanical review of some factors that are considered intuitive “proxies” of market efficiency (albeit incorrectly, according to recent studies and our own analysis). Such factors have little probative value and their review does not constitute the rigorous analysis demanded by the Supreme Court.

Instead, to invoke fraud-on-the-market, plaintiffs must first establish that the security traded in a weak-form efficient market (absent which a security cannot, as a logical matter, trade in a “semi-strong form” efficient market, the standard required for reliance purposes) using well-accepted tests. Only then do event study results, which are commonly used to demonstrate “cause and effect” (i.e., prove that the security’s price reacted quickly to news – a hallmark of a semi-strong form efficient market), have any merit. Even then, to claim classwide reliance, plaintiffs must prove such cause-and-effect relationship throughout the class period, not simply on selected disclosure dates identified in the complaint as plaintiffs often do.

These issues have policy implications because, once a class is certified, defendants frequently settle to avoid the magnified costs and risks associated with a trial, and the merits of the case (including the proper application of legal presumptions) are rarely examined at a trial.