Rounding of Analyst Forecasts

2005 ◽  
Vol 80 (3) ◽  
pp. 805-823 ◽  
Author(s):  
Don Herrmann ◽  
Wayne B. Thomas
Keyword(s):  
Forecast Accuracy ◽  
Analyst Forecasts ◽  
Forecast Dispersion ◽  
Negative Relation ◽  
Consensus Forecasts ◽  
Earnings Per Share ◽  
Market Expectations

We find that analyst forecasts of earnings per share occur in nickel intervals at a much greater frequency than do actual earnings per share. Analysts who round their earnings per share forecasts to nickel intervals exhibit characteristics of analysts who are less informed, exert less effort, and have fewer resources. Rounded forecasts are less accurate and the negative relation between rounding and forecast accuracy increases as the rounding interval increases from nickel to dime, quarter, half-dollar, and dollar. An examination of announcement period returns reveals that market expectations more closely align with consensus forecasts including rounded forecasts and then correct toward the more accurate consensus forecasts excluding rounded forecasts. Finally, exclusion of rounded forecasts decreases forecast dispersion.

scholarly journals Seeking Consensus: A New Approach

2010 ◽  
Vol 138 (12) ◽  
pp. 4402-4415 ◽  
Author(s):  
Paul J. Roebber
Keyword(s):  
Minimum Temperature ◽  
Forecast Accuracy ◽  
Cost Savings ◽  
Absolute Error ◽  
Electricity Production ◽  
Forecast Errors ◽  
Consensus Forecasts ◽  
Temperature Forecast ◽  
Model Output Statistics ◽  
The Individual

Abstract Simulated evolution is used to generate consensus forecasts of next-day minimum temperature for a site in Ohio. The evolved forecast algorithm logic is interpretable in terms of physics that might be accounted for by experienced forecasters, but the logic of the individual algorithms that form the consensus is unique. As a result, evolved program consensus forecasts produce substantial increases in forecast accuracy relative to forecast benchmarks such as model output statistics (MOS) and those from the National Weather Service (NWS). The best consensus produces a mean absolute error (MAE) of 2.98°F on an independent test dataset, representing a 27% improvement relative to MOS. These results translate to potential annual cost savings for electricity production in the state of Ohio of the order of $2 million relative to the NWS forecasts. Perfect forecasts provide nearly $6 million in additional annual electricity production cost savings relative to the evolved program consensus. The frequency of outlier events (forecast busts) falls from 24% using NWS to 16% using the evolved program consensus. Information on when busts are most likely can be provided through a logistic regression equation with two variables: forecast wind speed and the deviation of the NWS minimum temperature forecast from persistence. A forecast of a bust is 4 times more likely to be correct than wrong, suggesting some utility in anticipating the most egregious forecast errors. Discussion concerning the probabilistic applications of evolved programs, the application of this technique to other forecast problems, and the relevance of these findings to the future role of human forecasting is provided.

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.

Goodwill Impairment Charges and Analyst Forecast Properties

2014 ◽  
Vol 29 (1) ◽  
pp. 141-169 ◽  
Author(s):  
Lucy Huajing Chen ◽  
Jayanthi Krishnan ◽  
Heibatollah Sami
Keyword(s):  
Propensity Scores ◽  
Forecast Accuracy ◽  
Test Sample ◽  
Analyst Forecast ◽  
Forecast Dispersion ◽  
Industry Specialization ◽  
Goodwill Impairment ◽  
Auditor Industry Specialization ◽  
And Performance ◽  
Control Samples

SYNOPSIS We examine the association between goodwill impairment charges and analysts' forecast accuracy and dispersion. We compare a test sample of firm-quarters with reported goodwill impairment charges during 2003–2007, and two control samples (matched on propensity scores and performance) of firm-quarters that do not report impairment charges. We find that analysts' forecasts are less accurate and more dispersed for the impairment sample than for the control samples. The magnitude of impairment charges is also negatively associated with forecast accuracy and positively associated with forecast dispersion. However, two forms of monitoring, auditor industry specialization and institutional ownership, reduce the adverse effect of goodwill impairments on analyst forecast dispersion. JEL Classifications: G14; M41; M44

Audit Quality and Properties of Analyst Earnings Forecasts

2008 ◽  
Vol 83 (2) ◽  
pp. 327-349 ◽  
Author(s):  
Bruce K. Behn ◽  
Jong-Hag Choi ◽  
Tony Kang
Keyword(s):  
Financial Reporting ◽  
Audit Quality ◽  
Forecast Accuracy ◽  
Reporting Quality ◽  
Earnings Forecasts ◽  
Earnings Forecast ◽  
Forecast Dispersion ◽  
Industry Specialization ◽  
Big 5 ◽  
Analyst Earnings

Under the assumption that audit quality relates positively to unobservable financial reporting quality, we investigate whether audit quality is associated with the predictability of accounting earnings by focusing on analyst earnings forecast properties. The evidence shows that analysts' earnings forecast accuracy is higher and the forecast dispersion is smaller for firms audited by a Big 5 auditor. We further find that auditor industry specialization is associated with higher forecast accuracy and less forecast dispersion in the non-Big 5 auditor sample but not in the Big 5 auditor sample. Overall, our results suggest that high-quality audit provided by Big 5 auditors and industry specialist non-Big 5 auditors is associated with better forecasting performance by analysts.

scholarly journals Complexity is never simple: Intangible intensity and analyst accuracy

2020 ◽  
pp. 234094442093187
Author(s):  
Elena Ferrer ◽  
Rafael Santamaría ◽  
Nuria Suárez
Keyword(s):  
Financial Reporting ◽  
Sample Selection ◽  
Forecast Accuracy ◽  
Reporting Quality ◽  
Analyst Forecasts ◽  
Jel Classification ◽  
Firm Risk ◽  
Intensity Measures ◽  
Financial Reporting Standards ◽  
The Relationship

We examine the relationship between intangible intensity and the accuracy of analyst forecasts. Using an international sample of 2,200 firms during 2000–2016, we show that analyst accuracy decreases significantly when intangible intensity grows. In exploring the determinants of this effect, we distinguish between firm risk and the risk associated with intangibles. Our results reveal the role of financial reporting quality, ownership structure, and institutional quality in moderating the relationship between intangible intensity and analyst accuracy. Analyst forecast accuracy acts as a channel through which the higher levels of information asymmetry associated with intangible intensity affect the cost of equity. Our results are robust to different intangible intensity measures; mandatory changes in financial reporting standards; the implementation of transparency rules in certain industry sectors; and financial crisis periods. We have devised alternative econometric tools that deal with potential sample selection bias and the dynamics of our empirical model. JEL CLASSIFICATION: G00, G14, G30, M41

The Effect of Disclosures by Management, Analysts, and Business Press on Cost of Capital, Return Volatility, and Analyst Forecasts: A Study Using Content Analysis

2009 ◽  
Vol 84 (5) ◽  
pp. 1639-1670 ◽  
Author(s):  
S. P. Kothari ◽  
Xu Li ◽  
James E. Short
Keyword(s):  
Content Analysis ◽  
Cost Of Capital ◽  
Risk Measures ◽  
Analyst Forecasts ◽  
Return Volatility ◽  
Forecast Dispersion ◽  
Risk Effects ◽  
Business Press ◽  
Print Medium ◽  
The Cost

ABSTRACT: We document systematic evidence of risk effects of disclosures culled from a virtually exhaustive set of sources from the print medium. We content analyze more than 100,000 disclosure reports by management, analysts, and news reporters (i.e., business press) in constructing firm-specific disclosure measures that are quantitative and amenable to replication. We expect credibility and timeliness differences in the disclosures by source, which would translate into differential cost of capital effects. We find that when content analysis indicates favorable disclosures, the firm's risk, as proxied by the cost of capital, stock return volatility, and analyst forecast dispersion, declines significantly. In contrast, unfavorable disclosures are accompanied by significant increases in risk measures. Analysis of disclosures by source—corporations, analysts, and the business press—reveals that negative disclosures from business press sources result in increased cost of capital and return volatility, and favorable reports from business press reduce the cost of capital and return volatility.

Does Competition Improve Analysts’ Forecast Informativeness?

2020 ◽  
Author(s):  
Sanjay Banerjee
Keyword(s):  
Forecast Accuracy ◽  
Analyst Forecasts ◽  
The State ◽  
Private Signals ◽  
Economic State ◽  
Higher Rank ◽  
Signal Correlation

This paper studies the effect of competition on analysts’ forecast informativeness. Analysts compete to achieve a higher rank in forecast accuracy. Competition can impair, improve, or have no effect on analysts’ forecast informativeness. Competition impairs informativeness if and only if the prior uncertainty of the economic state is high, and analysts’ private signals are positively correlated conditional on the state. The more intense the competition in terms of a higher prize-to-penalty ratio and a stronger signal correlation, the less informative the forecasts. The higher the number of competing analysts, the greater the likelihood that analyst forecasts are less informative. This paper was accepted by Suraj Srinivasan, accounting.

scholarly journals The Earnings Expectations Game and the Dispersion Anomaly

2021 ◽  
Author(s):  
David Veenman ◽  
Patrick Verwijmeren
Keyword(s):  
Stock Returns ◽  
Return Predictability ◽  
Analyst Forecast ◽  
Forecast Dispersion ◽  
Earnings Expectations ◽  
Negative Relation ◽  
Earnings Surprises ◽  
Ex Ante ◽  
New Evidence

This study examines the role of differences in firms’ propensity to meet earnings expectations in explaining why firms with high analyst forecast dispersion experience relatively low future stock returns. We first demonstrate that the negative relation between dispersion and returns is concentrated around earnings announcements. Next, we show that this relation disappears when we control for ex ante measures of firms’ propensity to meet earnings expectations and that the component of dispersion explained by these measures drives the return predictability of dispersion. We further demonstrate that firms with low analyst dispersion are substantially more likely to achieve positive earnings surprises and provide new evidence consistent with both expectations management and strategic forecast pessimism explaining this result. Overall, we conclude that investor mispricing of firms’ participation in the earnings-expectations game provides a viable explanation for the dispersion anomaly. Accepted by Brian Bushee, accounting.

scholarly journals Fair value intensity and analyst forecasts

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Wessel M. Badenhorst
Keyword(s):  
Forecast Accuracy ◽  
Earnings Forecasts ◽  
Fair Value ◽  
Analyst Forecasts ◽  
The United States ◽  
Book Value ◽  
The United Kingdom ◽  
Value Measurement ◽  
The Impact ◽  
Book Values

Analysts’ earnings and book value forecasts play an important role in price discovery in equity markets. As the role of fair value measurements in accounting increases, the impact on analysts’ ability to accurately forecast earnings and book values is unclear. This article develops a method to calculate the degree of fair value measurement in financial statements and investigates the impact thereof on the accuracy of analysts’ book value and earnings forecasts, using a sample of firms listed in the United States and the United Kingdom from 2010 to 2014. Relying on multivariate regression findings, the article shows that greater fair value intensity decreases the 12-month analyst forecast accuracy for earnings in both countries. Moreover, there is some evidence that higher fair value intensity decreases the accuracy of analysts’ book value forecasts. It therefore appears that increased fair value intensity under a mixed measurement approach limits the ability of analysts to forecast earnings, without a compensating impact on forecasts of book values.

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