Analyst Uncertainty Research Articles

Probability and confidence: How to improve communication of uncertainty about uncertainty in intelligence analysis

AbstractIntelligence agencies communicate uncertainty to decision‐makers through verbal probability phrases that correspond to numerical ranges (i.e., probability lexicons) and ordinal levels of confidence. However, decision‐makers may misinterpret the relationship between these concepts and form inappropriate interpretations of intelligence analysts' uncertainty. In two experiments, four ways of conveying second‐order probability to decision‐makers were compared: (a) probability and confidence phrases written in the text of a report, (b) the addition of a probability lexicon, (c) the addition of a probability lexicon that varied numerical ranges according to the level of confidence (i.e., revised lexicon), and (d) a probability phrase written in text followed by a numerical range that varied according to the level of confidence. The revised lexicon was expected to improve interpretations of second‐order probability. The 275 participants in Experiment 1 and 796 participants in Experiment 2 provided numerical estimates corresponding to analytic judgments provided in descriptions about three overseas military operations and also indicated their support for approving or delaying the operations. The results demonstrated that providing the numerical range in the text of the report or providing a probability lexicon, improved interpretations of probability phrases above the verbal phrase‐only condition, but not interpretations of confidence. Participants were unable to correctly interpret confidence with respect to the precision of their estimate intervals and their decisions about the operations. However, in Experiments 2 and 3 the effects on these variables of providing decision‐makers with information about the source of the analyst's uncertainty were examined. In Experiment 3 (n = 510), providing this information improved correspondence between confidence level and approval of the operation. Recommendations are provided regarding additional methods of improving decision‐makers' interpretation of second‐order probability conveyed in intelligence reporting.

Does Disclosure of Advertising Spending Help Investors and Analysts?

Publicly listed firms have discretion to disclose (or not) advertising spending in their annual (10-K) reports. The disclosure of advertising spending can provide valuable information because advertising is a leading indicator of future performance. However, estimates of advertising spending are available from data providers, arguably mitigating the need for its formal disclosure. This article argues that firms’ disclosure of advertising spending provides more complete and public information and therefore lowers investor uncertainty about future firm performance (idiosyncratic risk). Empirical analyses show that the effect is largely driven by the negative effect of disclosure of advertising spending on analyst uncertainty. Consistent with agency theory, the negative effect of the disclosure of advertising spending on analyst uncertainty is stronger for firms with more financial resources, firms with lower disclosure quality, and firms that are in more competitive industries. Additional analyses show that the disclosure of advertising spending has a significant positive effect on firm value in specific sectors. These results, therefore, identify an avenue for chief marketing officers to play a greater role in managing investor relations. In addition, they suggest strong merit for the Securities and Exchange Commission and the Financial Accounting Standards Board to reconsider current regulations governing advertising spending disclosure.

Informing or obfuscating stakeholders: Integrated reporting and the information environment

AbstractThe global development of integrated reporting (IR) is underpinned by the recognition of increased complexity of businesses and stakeholder demands for information relating to financial performance, management, corporate governance, and sustainability being provided in a single, coherent document which facilitates stakeholder decision making. This paper examines the lexical properties of IR following the introduction of the International Integrated Reporting Council (IIRC) Framework. Using an international sample, we find that when adopted voluntarily, IR are lengthier, use more complex language, and contain more boilerplate statements. Our findings suggest that without regulation, firms may continue to produce long and difficult to understand reports in fear of being perceived as omitting “bad news.” This fear might be justified as we find loss of analyst following and greater analyst uncertainty when voluntarily adopted IR is concise. In the regulated setting of South Africa, however, we find IR has become more concise, and firms that produce longer and more complex documents suffer from a deterioration of their information environment. Our findings suggest that regulators and practitioners need to be cognizant of the potential for an increased volume in reporting to obfuscate the message rather than inform stakeholders.

Hedge fund activism and analyst uncertainty

Using a sample of hedge fund activist events from 2001 to 2014, we assess analysts’ reaction to hedge fund activism by considering changes in their recommendations and their earnings forecast accuracy. Following the arrival of a hedge fund activist, a preponderance of recommendations move to or are reinstated at the Hold level, the accuracy of analyst earnings forecasts decreases, and there is a decline in analyst forecast activity. Overall, our findings suggest hedge fund activism increases analyst uncertainty, limiting their ability to add value in this setting.

The impact of the U.S. employment report on exchange rates

The impact of the U.S. Employment Report and analyst forecasts of that report’s major statistics on Pound/Dollar, Yen/Dollar, and Euro/Dollar exchange rates are explored. While the nonfarm payroll employment figure has the greatest impact, we find that the exchange rates also react to the announced revision to last month’s payroll figure and to the unemployment rate. In all three markets, the exchange rate response to the payroll employment figure is strongly conditioned on pre-release analyst uncertainty. The median analyst forecast from Bloomberg anticipates over 80% of the monthly variation in the payroll figure and is basically unbiased. The markets appear to respond to these analyst forecasts prior to the government release. Analyst forecast dispersion tends to increase following large forecast errors indicating that when the announced figure is far from what analysts expected, they tend to disagree on the implications for future payroll levels.

The Impact of the Employment Report and Forecasts Thereof on Fixed-Income Markets

The authors examine the impact that the monthly Employment Situation Report issued by the Bureau of Labor Statistics (BLS) and the analyst forecasts of that report have on the U.S. Treasury securities market. Surprise increases in total non-farm payroll employment lead to increases in interest rates (especially one- to five-year rates), and surprise decreases lead to smaller declines in interest rates. This interest rate reaction is conditioned on the level of analyst uncertainty about the coming report. Interest rates also react to subsequent revisions of the payroll employment figures. Analyst forecasts as compiled by Bloomberg are unbiased forecasts of the BLS numbers and correctly anticipate most employment level changes. Moreover, there is evidence that the markets react to these forecasts prior to the BLS release. The authors also find that the release of the employment report lowers market uncertainty about future interest rates.

Confucius confusion: analyst forecast dispersion and business cycles

PurposeFinancial analysts have been found to be overconfident. The purpose of this paper is to study the ramifications of that overconfidence on the dispersion of earnings estimates as a predictor of the US business cycle.Design/methodology/approachWhether aggregate analyst forecast dispersion contains information about turning points in business cycles, especially downturns, is examined by utilizing the analyst earnings forecast dispersion metric. The primary analysis derives from logit regression and Markov switching models. The analysis controls for sentiment (consumer confidence), output (industrial production), and financial indicators (stock returns and turnover). Analyst data come from Institutional Brokers Estimate System, while the economic data are available at the Federal Reserve Bank of St Louis Economic Data site.FindingsA rise in the dispersion of analyst forecasts is a significant predictor of turning points in the US business cycle. Financial analyst uncertainty of earnings estimate contains crucial information about the risks of US business cycle turning points. The results are consistent with some analysts becoming overconfident during the expansion period and misjudging the precision of their information, thus over or under weighting various sources of information. This causes the disagreement among analysts measured as dispersion.Originality/valueThis is the first study to show that analyst forecast dispersion contributions valuable information to predictions of economic downturns. In addition, that dispersion can be attributed to analyst overconfidence.

Foreshadowing as Impression Management: Illuminating the Path for Security Analysts

Research summary: Managers can disclose information to security analysts as a form of impression management, but doing so is problematic because competitors can use that same information at the expense of the firm. We identify an impression management technique we call foreshadowing, which refers to hinting about future potential strategic activity. Foreshadowing provides information of value to analysts that can influence their evaluations of a firm, but not so much information as to put the firm at a competitive disadvantage. We hypothesize and find that managers who foreshadow acquisition announcements receive fewer analyst downgrades following the announcements, especially when there is more analyst uncertainty about the firm. We also hypothesize and find that analysts' responses to foreshadowing positively influence the likelihood that managers eventually acquire other firms.Managerial summary: Security analysts are often suspicious when firms announce acquisitions as those announcements are cumbersome to analyze on short notice and raise questions about managerial motivations that might not represent the best interests of the firm. We find that managers can improve analyst reactions to acquisition announcements by disclosing some information of value to analysts—specifically by hinting that an acquisition could occur in the future. We refer to such hints as foreshadowing. Foreshadowing entails giving analysts information to reduce their suspicions and facilitate their analyses, but not so much information as to degrade the firm's competitive information advantage over other firms. Foreshadowing also allows managers the option to reconsider actually executing the acquisition if analysts respond negatively to its possibility. Copyright © 2017 John Wiley & Sons, Ltd.

Improving Financial Statement Footnotes: Evidence from Derivative and Hedging Disclosures

I investigate whether changes in derivative and hedging footnote disclosures required by SFAS 161 affect investor and analyst uncertainty. My study is motivated by accounting standard setters' and researchers' interest in disclosure effectiveness, and by prior research linking financial disclosures to measures of uncertainty. For a broad sample of firms, I use textual analysis to measure changes in the amount and salience of derivative and hedging information caused by SFAS 161. Using a difference-in-differences design to study the effects of these changes, I find that uncertainty is reduced for firms adopting SFAS 161. In addition, I show that this reduction tends to be greater for firms whose disclosures were more affected by SFAS 161, consistent with the new disclosures improving investor understanding. I also find that improvements to the qualitative and quantitative content of disclosures have more impact than improvements to disclosure salience.

Tips and Tells from Managers: How Analysts and the Market Read between the Lines of Conference Calls

Stock prices react significantly to the tone (negativity of words) managers use on earnings conference calls. This reaction reflects reasonably rational use of information. Tone surprise--the residual when negativity in managerial tone is regressed on the firm's recent economic performance and CEO fixed effects--predicts future earnings and analyst uncertainty. Prices move more, as hypothesized, in firms where tone surprise predicts more strongly. Experienced analysts respond appropriately in revising their forecasts; inexperienced analysts overreact (underreact) to tone surprises in presentations (answers). Post-call price drift, like post-earnings announcement drift, suggests less-than-full-use of information embedded in managerial tone.

The Impact of Eliminating the 20-F Reconciliation Requirement for IFRS Filers on Earnings Persistence and Information Uncertainty

SYNOPSIS: On November 15, 2007, the U.S. Securities and Exchange Commission (SEC) eliminated the requirement that foreign private issuers reporting under International Financial Reporting Standards (IFRS) include a reconciliation to U.S. GAAP in their 20-F filing. To the extent that the reconciliations had information content, it is possible that the information environment of IFRS filers deteriorated in the post-reconciliation period, unless they voluntarily improved disclosure quality. Using difference-in-differences tests, we examine whether there was any change in the persistence of earnings and analyst forecast dispersion after the new regulation. We find that earnings persistence increased (did not increase) and analyst uncertainty measured by the forecast dispersion did not increase (increased) for firms domiciled in weaker (stronger) investor protection countries. These results suggest that firms from a weaker investor protection environment had a greater incentive to “signal” the quality by voluntarily improving the disclosure quality in the post-reconciliation period to compensate for any possible information loss from no longer providing the reconciliation. Our findings also suggest that the elimination of the reconciliation requirement did not have a uniform effect on IFRS filers and that the effect varies with the firm's home country reporting environment. JEL Classifications: M41

Does the Capitalization of Development Costs Improve Analyst Forecast Accuracy? Evidence from the UK

AbstractIt has been documented that investments in Research and Development (R&D) are associated with increased errors and inaccuracy in earnings forecasts made by financial analysts. These deficiencies have been generally attributed to information complexity and the uncertainty of the future benefits of R&D. This paper examines whether the capitalization of development costs can reduce analyst uncertainty about the future economic outcome of R&D investments, provide outsiders with a better matching of future R&D‐related revenues and costs, and therefore promote accuracy in analyst forecasts. UK data is used, because accounting rules in the United Kingdom permitted firms to conditionally capitalize development costs even before the introduction of the International Financial Reporting Standards. The choice to expense R&D rather than conditionally capitalize development costs is found to relate positively to signed analyst forecast errors. This finding is robust to controlling for the influence of other factors that may affect errors, as well as for the influence of R&D investments on forecast errors. The decision to capitalize versus expense is not observed to have a significant influence on analyst forecast revisions. The findings are interpreted as evidence that the choice to capitalize as opposed to expense may help to reduce deficiencies in analyst forecasts; hence, is informative for users of financial statements. Increased informativeness is expected to have repercussions for the effectiveness with which analysts produce earnings forecasts, and, as a result, market efficiency.

The Effect of Accounting Policy Disclosure Length on Investor and Analyst Uncertainty

The Effect of Accounting Policy Disclosure Length on Investor and Analyst Uncertainty

Voluntary Disclosure in R&D‐Intensive Industries*

Disclosures about R&D activities could potentially help market participants understand the future prospects of R&D intensive firms, but at the same time could be costly to make if the disclosure is related to proprietary information. I examine R&D-related disclosures made by R&D intensive firms in their annual report, as well as throughout the year to financial analysts. I find that the level of R&D-related voluntary disclosure is higher when proprietary costs are lower and when the book to market ratio is lower, perhaps because the basic financial statements are less informative about market value. In addition, after controlling for the level of general disclosure and forward looking disclosure, I find a negative relation between disclosures about development stage R&D and both analysts' one-year-ahead sales forecast error and dispersion. This is consistent with disclosures about development stage R&D reducing analyst uncertainty about one-year-ahead sales. I find mixed evidence about earnings forecasts. A higher level of disclosure about both R&D projects in progress and development stage R&D is associated with less error in analysts' one-year-ahead earnings forecasts. However, I find no evidence of a relation between R&D-related disclosure and the dispersion in one-year-ahead earnings forecasts.

Security Analyst Incentives and Quality of Analyst Generated Information

We investigate the relationship between security analyst coverage characteristics and analyst-related incentives in order to determine whether the quality of analyst-generated information, (as measured by the absolute median forecast error of estimates, analyst uncertainty, diversity of opinion among analysts, and excessive analyst optimism) improves if higher trading commissions and fees can be earned, or when there is greater investor interest and scrutiny. We find strong evidence that incentives influence the quality of analyst information. The implication of our findings is that it would serve investors well to distinguish between high quality and low quality analyst forecasts and recommendations. We recommend that investors estimate the “information risk” associated with analyst reports. <b>TOPICS:</b>Security analysis and valuation, fundamental equity analysis

An alternative approach for choice models in transportation: Use of possibility theory for comparison of utilities

Modeling of human choice mechanism has been a topic of intense discussion in the transportation community for many years. The framework of modeling has been rooted in probability theory in which the analyst?s uncertainty about the integrity of the model is expressed in probability. In most choice situations, the decision-maker (traveler) also experiences uncertainty because of the lack of complete information on the choices. In the traditional modeling framework, the uncertainty of the analyst and that of the decision-maker are both embedded in the same random term and not clearly separated. While the analyst's uncertainty may be represented by probability due to the statistical nature of events, that of the decision maker, however, is not always subjected to randomness; rather, it is the perceptive uncertainty. This paper proposes a modeling framework that attempts to account for the decision maker?s uncertainty by possibility theory and then the analyst's uncertainty by probability theory. The possibility to probability transformation is performed using the principle of uncertainty invariance. The proposed approach accounts for the quality of information on the changes in choice probability. The paper discusses the thought process, mathematics of possibility theory and probability transformation, and examples.

The analyst's uncertainty and fear. Panel report.

The analyst's uncertainty and fear. Panel report.

High‐Technology Intangibles and Analysts’ Forecasts

This study examines the association between firms’ intangible assets and properties of the information contained in analysts’ earnings forecasts. We hypothesize that analysts will supplement firms’ financial information by placing greater relative emphasis on their own private (or idiosyncratic) information when deriving their earnings forecasts for firms with significant intangible assets. Our evidence is consistent with this hypothesis. We find that the consensus in analysts’ forecasts, measured as the correlation in analysts’ forecast errors, is negatively associated with a firm’s level of intangible assets. This result is robust to controlling for analyst uncertainty about a firm’s future earnings, which we also find to be higher for firms with high levels of internally generated (and expensed) intangibles. Given that analyst uncertainty increases and analyst consensus decreases with the level of a firm’s intangible assets, we also expect and find that the degree to which the mean forecast aggregates private information and is more accurate than an individual analyst’s forecast increases with a firm’s intangible assets. Finally, additional analysis reveals that lower levels of analyst consensus are associated with high‐technology manufacturing companies, and that this association is explained by the relatively high R&amp;D expenditures made by these firms. Overall, our results are consistent with financial analysts augmenting the financial reporting systems of firms with higher levels of intangible assets (in terms of contributing to more accurate earnings expectations), particularly R&amp;D‐driven high‐tech manufacturers.

The opportunity, gravity and thurstone models of individual choice

This paper traces the development of methods for analyzing people's choice of action. It shows that two empirical formulations, the gravity and opportunity models, are approximate solutions to a more natural description of individual behavior, called the Thurstone model. In this model, each person always picks the course of action that he thinks is the best available. The analyst, however, cannot predict the choice because he does not know the individual's exact value for each alternative. Nevertheless, the analyst can describe these values with probability distributions, and by appropriate calculations can determine the chance of each alternative being chosen. The difference between the three models arises in the method of describing the analyst's uncertainty about a person's values. The Thurstone model uses an independent Normal distribution for each alternative. The gravity model simplifies the calculations by substituting an approximation for this distribution. The opportunity model is the special form of the gravity model that occurs when the values of the opportunities are dispersed at uniform intervals. The greater accuracy of the Thurstone model has been confirmed by a test with data on automobile parking choices. Nevertheless, the gravity model is useful for preliminary calculations because it is easier to calculate. Although the opportunity model usually is less accurate than either of the other two models, the difference is negligible in many common situations.