Does an Analyst's Access to Information Vary with the Favorableness of Their Language When Speaking to Management?

2017 ◽  
Vol 31 (4) ◽  
pp. 13-31 ◽  
Author(s):  
Jonathan A. Milian ◽  
Antoinette L. Smith ◽  
Elio Alfonso
Keyword(s):  
Earnings Forecasts ◽  
Earnings Forecast ◽  
Access To Information ◽  
Positive Relation ◽  
Conference Calls ◽  
Large Sample ◽  
Quarterly Earnings ◽  
Similar Relation ◽  
General Experience ◽  
Brokerage Firms

SYNOPSIS We examine whether analysts who use more favorable language during earnings conference calls subsequently issue more accurate earnings forecasts. Using a large sample of earnings conference calls from the 2004–2013 period for S&P 500 firms, we find a significantly positive relation between an analyst's tone during a firm's call and the accuracy of the analyst's next quarterly earnings forecast for that firm. We find a similar relation for analysts who praise a firm's management during the call. Our findings are consistent with the favorableness of an analyst's language reflecting their access to a firm's management. In additional analyses, we find that female analysts, analysts with less general experience, analysts at smaller brokerage firms, and analysts who cover more industries, on average, use significantly more favorable language during earnings conference calls. Overall, we contribute a new proxy, incremental to other proxies, for the analyst-manager relationship.

Stock Recommendations as a Source of Bias in Earnings Forecasts

2006 ◽  
Vol 18 (1) ◽  
pp. 37-51 ◽  
Author(s):  
Michael J. Eames ◽  
Steven M. Glover ◽  
Jane Jollineau Kennedy
Keyword(s):  
Cognitive Processes ◽  
Earnings Forecasts ◽  
Economic Incentives ◽  
Securities And Exchange Commission ◽  
Earnings Forecast ◽  
Systematic Bias ◽  
Stock Recommendations ◽  
Forecast Bias ◽  
Sarbanes Oxley ◽  
Brokerage Firms

Recent scandals and controversies have focused substantial attention on the behavior of financial analysts. Responses such as the Sarbanes-Oxley Act, new regulations at securities exchanges, and massive legal settlements are consistent with the perception that analysts' research and stock recommendations exhibit significant self-serving bias. While anecdotal and legal evidence support the allegations that some analysts have intentionally mislead the investing public, recent archival research suggests unintentional cognitive processes also contribute to systematic bias in analysts' forecasts (Eames et al. 2002). However, studies based on stock-market data cannot distinguish between unintentional cognitive processes and intentional bias stemming from economic incentives (e.g., trade boosting). In a laboratory experiment we eliminate economic incentives and find that cognitive processes unintentionally lead to earnings forecast bias. Our results suggest that recent regulations and policy changes by Congress, the Securities and Exchange Commission, exchange markets, and brokerage firms will not totally eliminate bias in analysts' earnings forecasts.

Short-term management earnings forecasts and earnings management through real activities manipulation

2019 ◽  
Vol 28 (1) ◽  
pp. 110-138
Author(s):  
Bing Luo
Keyword(s):  
Earnings Management ◽  
Earnings Forecasts ◽  
Earnings Forecast ◽  
Management Earnings Forecasts ◽  
Short Term ◽  
Content Type ◽  
Quarterly Earnings ◽  
Real Activities Manipulation ◽  
External Shareholders ◽  
Term Management

Purpose The purpose of this paper is to investigate the association between managers’ short-term, quarterly earnings forecast characteristics and earnings management through real activities manipulation. Design/methodology/approach Using a propensity-score matched sample from 2000 to 2015, the author examines whether, compared to non-issuers, firms issuing short-term earnings forecasts exhibit abnormal levels of earnings management through the manipulation of real activities such as acceleration of sales, changes in shipment schedules and delaying R&D and maintenance expenditures. Findings The finding of this study suggests that firms actually engage in less real activities manipulation when they provide short-term management earnings forecasts. This result does not support the practitioners’ criticism that providing short-term management earnings forecasts increases earnings management. Instead, it suggests that providing management earnings forecasts can reduce information asymmetry between managers and external shareholders, thereby constraining managers’ opportunistic behaviors. Originality/value Practitioners have expressed concerns that issuing earnings forecasts may foster managerial myopia, therefore, increasing earnings management. However, recent empirical study found evidence that management earnings forecast mitigates accrual-based earnings management, which is inconsistent with practitioners’ view. This study hence aims to provide timely evidence to this debate by examining the relation between management earnings forecasts and real activities manipulation.

Refining financial analysts’ forecasts by predicting earnings forecast errors

2017 ◽  
Vol 25 (2) ◽  
pp. 256-272 ◽  
Author(s):  
Tatiana Fedyk
Keyword(s):  
Serial Correlation ◽  
Design Methodology ◽  
Financial Analysts ◽  
Earnings Forecasts ◽  
Autoregressive Process ◽  
Earnings Forecast ◽  
Forecast Errors ◽  
Content Type ◽  
Quarterly Earnings ◽  
Prior Literature

Purpose The purpose of this paper is to examine the way serial correlation in quarterly earnings forecast errors varies with firm and analyst attributes such as the firm’s industry and the analyst’s experience and brokerage house affiliation. Prior research on financial analysts’ quarterly earnings forecasts has documented serial correlation in forecast errors. Design/methodology/approach Finding that serial correlation in forecast errors is significant and seemingly independent of firm and analyst attributes, the consensus forecast errors are modeled as an autoregressive process. The model of forecast errors that best fits the data is AR(1), and the obtained autoregressive coefficients are used to predict consensus forecast errors. Findings Modeling the consensus forecast errors as an autoregressive process, the present study predicts future consensus forecast errors and proposes a series of refinements to the consensus. Originality/value These refinements were not presented in prior literature and can be useful to financial analysts and investors.

Concurrent Earnings Announcements and Analysts' Information Production

2019 ◽  
Vol 95 (1) ◽  
pp. 165-189 ◽  
Author(s):  
Matthew Driskill ◽  
Marcus P. Kirk ◽  
Jennifer Wu Tucker
Keyword(s):  
Earnings Announcements ◽  
Financial Analysts ◽  
Earnings Forecasts ◽  
Data Availability ◽  
Limited Attention ◽  
Conference Calls ◽  
Stock Recommendations ◽  
Rich Information ◽  
Sample Firm ◽  
Jel Classifications

ABSTRACT We examine whether financial analysts are subject to limited attention. We find that when analysts have another firm in their coverage portfolio announcing earnings on the same day as the sample firm (a “concurrent announcement”), they are less likely to issue timely earnings forecasts for the sample firm's subsequent quarter than analysts without a concurrent announcement. Among the analysts who issue timely earnings forecasts, the thoroughness of their work decreases as their number of concurrent announcements increases. In addition, analysts are more sluggish in providing stock recommendations and less likely to ask questions in earnings conference calls as their number of concurrent announcements increases. Moreover, when analysts face concurrent announcements, they tend to allocate their limited attention to firms that already have rich information environments, leaving behind firms in need of attention. Overall, our evidence suggests that even financial analysts, who serve as information specialists, are subject to limited attention. JEL Classifications: G10; G11; G17; G14. Data Availability: Data are publicly available from the sources identified in the paper.

Discussion: “Dispersion in Analysts' Earnings Forecasts as a Measure of Uncertainty”

1998 ◽  
Vol 13 (3) ◽  
pp. 271-274 ◽  
Author(s):  
Lawrence D. Brown
Keyword(s):  
Stock Price ◽  
Annual Report ◽  
Earnings Forecasts ◽  
Analyst Forecast ◽  
Earnings Forecast ◽  
Data Set ◽  
Forecast Dispersion ◽  
Forecast Revision ◽  
Analyst Earnings ◽  
The Impact

This paper tackles an interesting question; namely, whether dispersion in analysts' earnings forecasts reflects uncertainty about firms' future economic performance. It improves on the extant literature in three ways. First, it uses detailed analyst earnings forecast data to estimate analyst forecast dispersion and revision. The contrasting evidence of Morse, Stephan, and Stice (1991) and Brown and Han (1992), who respectively used consensus and detailed analyst data to examine the impact of earnings announcements on forecast dispersion, suggest that detailed data are preferable for determining the data set on which analysts' forecasts are conditioned. Second, it relates forecast dispersion to both analyst earnings forecast revision and stock price reaction to the subsequent earnings announcement. Previous studies related forecast dispersion to either analyst forecast revision (e.g., Stickel 1989) or to subsequent stock price movements (e.g., Daley et al. [1988]), but not to both revision and returns. Third, it includes the interim quarters along with the annual report. In contrast, previous research focused on the annual report, ignoring the interims (Daley et al. [1988]).

Effects of an Advancing Tenure on CEO Cognitive Complexity

2020 ◽  
Vol 31 (4) ◽  
pp. 936-959 ◽  
Author(s):  
Lorenz Graf-Vlachy ◽  
Jonathan Bundy ◽  
Donald C. Hambrick
Keyword(s):  
Chief Executive Officers ◽  
Cognitive Complexity ◽  
Theory Of Measurement ◽  
Reliability And Validity ◽  
Chief Executive ◽  
Conference Calls ◽  
Public Corporations ◽  
Ceo Tenure ◽  
General Experience ◽  
Executive Officers

We study how the cognitive complexity of chief executive officers (CEOs) changes during their tenures. Drawing from prior theory and research, we argue that CEOs attain gradually greater role-specific knowledge, or expertise, as their tenures advance, which yields more complex thinking. Beyond examining the main effect of CEO tenure on cognitive complexity, we consider three moderators of this relationship, each of which is expected to influence the accumulation of expertise over a CEO’s time in office: industry dynamism, industry jolts, and CEO positional power. We conduct our tests on a sample of 684 CEOs of public corporations. The analytic centerpiece of our study is a novel index of CEO cognitive complexity based on CEOs’ language patterns in the question-and-answer portions of quarterly conference calls. As part of our extensive theory of measurement, we provide evidence of the reliability and validity of our index. Our results indicate that CEOs, in general, experience substantial increases in cognitive complexity over their time in office. Examined moderators somewhat, but modestly, alter this general trajectory, and nonlinearities are not observed. We discuss the implications of our findings.

Do Analysts Say Anything About Earnings Without Revising Their Earnings Forecasts?

2018 ◽  
Vol 94 (2) ◽  
pp. 29-52 ◽  
Author(s):  
Philip G. Berger ◽  
Charles G. Ham ◽  
Zachary R. Kaplan
Keyword(s):  
Earnings Forecasts ◽  
Share Price ◽  
Earnings Forecast ◽  
Forecast Dispersion ◽  
Earnings Announcement ◽  
Forecast Revisions ◽  
Predictable Returns

ABSTRACT Analysts are selective about which forecasts they update and, thus, convey information about current quarter earnings even when not revising the current quarter earnings (CQE) forecast. We find that (1) textual statements, (2) share price target revisions, and (3) future quarter earnings forecast revisions all predict error in the CQE forecast. We document several reasons analysts sometimes omit information from the CQE forecast: to facilitate beatable forecasts by suppressing positive news from the CQE forecast, to herd toward the consensus, and to avoid small forecast revisions. We also show that omitting information from CQE forecasts leads to lower forecast dispersion and predictable returns at the earnings announcement.

scholarly journals Detecting Information Pooling: Evidence from Earnings Forecasts after Brokerage Mergers

2007 ◽  
Vol 7 (1) ◽  
Author(s):  
Serena Ng ◽  
Matt Shum
Keyword(s):  
Earnings Forecasts ◽  
Information Pooling ◽  
Weak Evidence ◽  
Forecasting Performance ◽  
Brokerage Firms

Abstract Forecast improvements can be expected if the two partners involved in a brokerage merger pool information and expertise. We examine four large mergers of brokerage firms in the last decade to study the incidence of and explanations for forecast improvements after the mergers. At the brokerage-level, we find that for two of the four mergers, forecast improvements appear more pronounced in subsamples of stocks for which both of the pre-merger analysts were retained in the merged brokerage. At the analyst-level, we find only weak evidence of forecast improvements after the merger. However, we find evidence that after a merger, a stock is more likely to be assigned to an analyst with overall better forecasting performance before the merger. This suggests that analyst selection can be a mechanism generating the post-merger forecasting improvements.

The Moderating Effect of Industry Concentration on the Relations Between External Attributes and the Properties of Analyst Earnings Forecast

2013 ◽  
Vol 16 (03) ◽  
pp. 1350019 ◽  
Author(s):  
Yu-Cheng Chen ◽  
Chiung-Yao Huang ◽  
Pei-I Chou
Keyword(s):  
Forecast Accuracy ◽  
Earnings Forecasts ◽  
Moderating Effect ◽  
Industry Concentration ◽  
Earnings Forecast ◽  
Moderating Effects ◽  
Forecast Dispersion ◽  
Earnings Surprise ◽  
Using Data ◽  
Analyst Earnings

Based on the work of earlier studies, the main objective of this study is to determine whether the properties of analyst earnings forecast are related to the interaction effects of external attributes and industry concentration that were not the focus of previous research. Specifically, this study examines the relations between external attributions and the properties of analyst earnings forecasts. Furthermore, we explore the moderating effect of industry concentration on the relations between external attributions and the properties of analyst earnings forecasts. Using data from Compustat and I/B/E/S, we provide evidence that analysts' earnings forecast accuracy is lower and the forecast dispersion is larger for firms with more earnings surprise. Firms with more analysts' forecasts covering are associated with higher forecast accuracy, but not necessarily higher forecast dispersion. The moderating effects of industry concentration on the relationships between earnings surprise, the number of estimates covering the company and forecast accuracy are particularly strong. In addition, the moderating effects of industry concentration on the relationship between earnings surprise, the number of estimates covering the company and the forecast dispersion are partially supported. Overall, the industrial concentration factor either magnifies or alleviates the effect of external attributions on analyst's forecast accuracy and forecast dispersion.

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