An empirical evaluation of analysts’ herding behavior following Regulation Fair Disclosure

This study examines whether analysts’ forecasts exhibited increased herding behavior following the adoption of Regulation Fair Disclosure. A recent model by Arya et al. [Arya, A., Glover, J., Mittendorf, B., Narayanamoorthy, G., 2005. Unintended consequences of regulating disclosures: The case of Regulation Fair Disclosure. Journal of Accounting and Public Policy 24 (3), 243–252], using a discrete-time information cascade-based model, projects that one potential consequence of Regulation Fair Disclosure might be increased herding by financial analysts, although previous studies examining the economic consequences of Regulation FD have generally not found any averse consequence for investors. We examine financial analysts forecasting behavior before and after the adoption of Regulation FD in order to determine if such herding of forecasts occurred empirically. Our general finding is that increased herding behavior cannot be detected among either the firms most directly impacted by Regulation FD (those which used to hold closed press conferences), or those least affected (i.e., firms that used to either hold open or no press conferences). However, because analysts face diverse incentives for engaging in either herding or anti-herding behavior, our results are not interpretable as an empirical test of the Arya et al. (2005) theoretical model.     

Analyst Activity and Firm Value: Evidence from the REIT Sector

This paper is the first to examine (1) properties of analyst forecasts and (2) effects of analyst following on firm value for all REITs on CRSP, Compustat and I/B/E/S. Our results suggest that REITs operate in an information environment that has changed over time. We find that for periods when the REIT industry was either in the developmental stage (pre-1992), or after other structural changes in the industry (post-2000), more analysts cover REITs and forecasts are more accurate and less biased. Further, we find that mortgage REITs are more transparent than other REIT structures and exhibit properties of analyst behavior that are different from other types of REITs. Our investigation into the effect of analyst coverage on REIT value suggests that analyst coverage increases REIT value (as measured by Tobin’s q) and that the causality does not run the opposite way.

Analyst estimation revision clusters and corporate events, Part II

Summary In Part I of this study, we evaluated the relative usefulness of information in alternative corporate information events (CIEs) to analysts by examining the frequency with which they trigger clusters of analysts’ earnings estimate revisions. In Part II, we examine investor response to various CIEs and their revision clusters. We find that stock prices react most strongly and adjust most quickly to revision clusters that accompany CIEs that focus on financial statement information. CIEs that offer strategic information take longer for analysts and investors to assimilate, and investors appear to rely heavily on later analyst revisions following such events.     

Analyst estimation revision clusters and corporate events,Part I

Summary This paper uses periods of unusually heavy earnings estimate revision activity by analysts to assess the relative usefulness of corporate information events (CIEs) in firm valuation. Because accounting information is more readily available, newsworthy and accessible, we hypothesize that CIEs that focus on financial statement information trigger greater analyst revision activity over a shorter period of time than CIEs that offer strategic or “soft” information. Our results are consistent with this hypothesis. In Part II, we examine investor response to revision clusters that accompany different CIEs.We thank the Editor, an anonymous referee, Joe Cooper, Todd Doersch, Tony Greig, Kent Konkol and Marc Sievers for helpful suggestions and discussions. We also thank Mehmet Ozbilgin and James Su for programming during the planning phase of this project, and Jinyoung Park for research assistance during its execution. We are very grateful to Thomson Financial, CCBN and Reuters Data for providing data used in this study. Bagnoli and Watts thank the Krannert Graduate School of Management andPurdue University for financial support.     

Why Do Analysts use a Zero Forecast for Other Comprehensive Income?

Accounting theory and accounting researchers stress the importance of clean surplus accounting and comprehensive income to corporate valuation. However, casual observation suggests that sell-side equity analysts routinely ignore other comprehensive income (OCI) in their forecasts and instead focus on forecasting earnings (before OCI). Using a sample of analyst reports, I first confirm that analysts normally omit forecasts of OCI or comprehensive income from their reports, consistent with analysts forecasting OCI as zero. I then predict and find that a zero forecast for OCI generally produces lower forecasting errors than alternative time-series models, such as a random walk or AR(1) model, suggesting a rational reason why analysts take this approach. Finally, I predict and find that although analysts’ point forecasts of future OCI are usually zero, their implied cost of equity estimates are consistent with analysts forecasting a positive variance for OCI.     

The disparity between long-term and short-term forecasted earnings growth

We find the disparity between long-term and short-term analyst forecasted earnings growth is a robust predictor of future returns and long-term analyst forecast errors. After adjusting for industry characteristics, stocks whose long-term earnings growth forecasts are far above or far below their implied short-term forecasts for earnings growth have negative and positive subsequent risk-adjusted returns along with downward and upward revisions in long-term forecasted earnings growth, respectively. Additional results indicate that investor inattention toward firm-level changes in long-term earnings growth is responsible for these risk-adjusted returns.     

The determinants of analyst-firm pairings

This paper explores the determinants of observed analyst-firm pairings. We adopt an analyst/brokerage house perspective that allows us to examine not only firm-level characteristics as in prior research, but also attributes of the analyst and the analyst’s brokerage house that may drive these pairings. Our empirical analyses provide two primary insights. First, analyst characteristics such as industry expertise and relative experience, and brokerage house characteristics such as continuity of coverage, are associated with the decision to follow a firm. Second, there is substantial variation in the association between firm, analyst, and brokerage house characteristics and the decision to follow a firm; this occurs across individual analysts as well as across different types of brokerage houses. Overall, our results provide further insights into the factors leading to observed analyst-firm pairings, and indicate that these factors vary across analysts and their brokerage houses – suggesting richer associations than the average firm-level relationships documented by prior research.     

How efficient is the market for Australian firms’ earnings information?

We construct a measure of the speed with which forecasts issued by sell-side analysts accurately forecast future annual earnings. Following Marshall, we label this measure earnings information flow timeliness (EIFT). This measure avoids the aggregation problem inherent in price-based measures of information efficiency. We document large variation in EIFT across firm-years, and show that EIFT is positively associated with the extent of analyst following, consistent with increased analyst coverage improving the speed with which earnings-related information is recognised. We also find that EIFT is higher for firm-years classified as ‘bad news’ (i.e., where analysts’ forecasts at the start of the financial period exceed the reported outcome). However, when we separately consider instances where analysts appear to forecast non-GAAP (or ‘street’) earnings rather than GAAP earnings, we find that the greater timeliness of bad news is concentrated among observations where analysts forecast non-GAAP earnings, where unusual items are typically excluded. We conclude that the market for accounting information is more efficient for negative operating outcomes than for negative outcomes reflecting unusual items.     

互联网金融特质性风险及监管政策研究

互联网金融作为金融业融合互联网衍生形成的一种新型金融模式,其具有技术 性、技术与制度二重性以及认知性等特质性风险。本文在分析互联网金融特质性风险的基础 上,对已颁布的监管政策进行梳理,揭示目前监管政策的缺陷,提出监管政策改进建议：健全 和完善互联网金融监管的规章制度;引入监管沙盒试点,紧扣“四个核心”的监管沙盒机制来 助推金融创新;从政府、企业与行业协会三方着手,构建市场准入与退出机制;加强互联网金 融消费者权益保障。     

Ownership structure and forecast accuracy in Spain

This article examines how analysts respond to specific ownership structure characteristics by studying the accuracy of their forecasts after the release of the first Spanish corporate governance code and before IFRS were adopted. Specifically, we analyse the influence of ownership concentration, bank ownership and insider ownership on analyst forecast errors. Overall the results show a positive and significant influence of bank ownership on analyst forecast accuracy, which suggests that bank ownership leads to closer monitoring of management and a reduction in analyst forecast errors. However, the presence of large shareholders and insiders in the ownership structure of the firm does not significantly affect the accuracy of financial analysts. This research provides investors with a more refined sense of how analyst forecasts might be affected through the composition of the ownership structure in a context of high concentration of ownership, relevant presence of banks in firms as creditors and shareholders, and local GAAP.