Individual Analysts Research Articles

Do Investors Fully Unravel Persistent Pessimism in Analysts' Earnings Forecasts?

ABSTRACT This study presents evidence suggesting that investors do not fully unravel predictable pessimism in sell-side analysts' earnings forecasts. We show that measures of prior consensus and individual analyst forecast pessimism are predictive of both the sign of firms' earnings surprises and the stock returns around earnings announcements. That is, we find that firms with a relatively high probability of forecast pessimism experience significantly higher announcement returns than those with a low probability. Importantly, we show that these findings are driven by predictable pessimism in analysts' short-term forecasts, as opposed to optimism in their longer-term forecasts. We further find that this mispricing is related to the difficulty investors have in identifying differences in expected forecast pessimism. Overall, we conclude that market prices do not fully reflect the conditional probability that a firm meets or beats earnings expectations as a result of analysts' pessimistically biased short-term forecasts. JEL Classifications: G12; G14; G20.

Impact of strategy on analyst information

In this paper, we investigate the impact of firm strategy on the properties of analyst' information. We argue that analysts' total information (common and idiosyncratic information together) about a firm depends on how clearly evident the chosen strategy of a firm is. Second, we argue that financial analysts will see more opportunities for value addition in differentiators, and hence, will gravitate more towards such firms. Analysts add value by gathering private information and, thus, individual analyst's private information will be a greater percentage of total information for a firm pursuing a differentiation strategy than for a firm pursuing a cost leadership strategy. Our results confirm our hypotheses.

The Effect of an Individual analyst’s Information Environments on Analysts’ Forecast Accuracy after Mandatory IFRS Adoption

본 연구는 국제회계기준(이하 IFRS)을 의무도입한 우리나라 기업들을 대상으로 개별 재무분석가들이 IFRS에 의해 작성된 재무제표를 해석함에 있어서 의견일치정도가 증가하는지 또는 의견불일치정도가 증가하는지를 Barron et al.(1998)이 제안한 공통 정보 활용도(BKLS Consensus) 측정치를 통해 살펴보고, 이러한 개별 재무분석가들의 행태가 이익예측정확도를 높이는데 기여하는지 검증하였다. 재무분석가들은 공적(public) 혹은 사적(private) 경로를 통하여 기업 분석을 위한 정보를 획득하고, 이러한 정보를 동일하게 해석하는 경우 공통 정보(common information)로 남게 되지만, 개별 재무분석가별로 서로 달리 해석하는 경우 고유 정보(idiosyncratic information)로 전환될 수 있다.BR 본 연구 결과 IFRS 도입 이후 개별 재무분석가들은 의견불일치정도가 증가하였다. 즉, 공통 정보 이외에 고유 정보를 더욱 활용하였으나 회계이익의 질이 높은 기업의 경우 공통 정보 활용 비중이 증가하였다. 또한 IFRS 도입 이후 개별 재무분석가들이 고유 정보 활용 비중을 높이는 것은 이익예측 정확도를 높이는데 기여하였으나, 회계이익의 질이 높은 기업의 경우는 공통 정보의 활용으로도 이익예측 정확도를 높이고 있음을 확인하였다. IFRS는 연결재무제표와 공정가치 평가를 기본으로 하며, 상세하고 구체적인 회계처리 방법을 제시하기 보다는 원칙중심의 기준체계를 가지고 있기 때문에, 경영자의 판단으로 회계처리 방법을 선택할 수 있다. 따라서 IFRS 도입 이후 개별 재무분석가들은 공통 정보 이외에 보다 차별화된 고유 정보를 활용하여 수집한 정보를 해석하고 있음을 알 수 있다. 그러나 고유 정보 수집에는 추가적인 시간과 노력이 필요하기 때문에 본 논문은 IFRS 도입 이후 기업이 제공하는 회계정보의 양과 질에 따라 개별 재무분석가는 공통정보와 고유 정보 활용 전략을 세우는 것이 예측 정확도 향상에 효과적임을 시사한다.

Does Forecast Bias Affect Financial Analysts’ Market Influence?

Prior studies find that analysts tend to bias their forecasts upward in poor information environments and downward in rich information environments, consistent with attempts to curry favor with management. We find that investors anticipate this behavior by reducing their response to upward forecasts in poor information environments and downward forecasts in rich information environments. Using Hugon and Muslu’s measure of analyst conservatism as an ex ante indicator of individual analysts’ forecast bias tendencies, we show that the stronger return response they find to conservative analysts’ forecast revisions is restricted to poor information environments, where optimistic analyst bias is prevalent. Our results suggest that analysts pay a price in market influence when their forecasts reinforce analysts’ typical forecast bias for the firm’s information environment. Conversely, analysts whose forecasts conflict with the typical bias for the firm are rewarded with larger than average return responses.

The many faces of a rebel group: the Allied Democratic Forces in the Democratic Republic of Congo

This article discusses the case of the Allied Democratic Forces (ADF) rebel group in the Democratic Republic of Congo (DRC). It shows how a variety of actors that have opposed the ADF group have framed the rebels to achieve a range of political and economic objectives, or in response to organizational and individual limitations. The DRC and Ugandan governments have each framed ADF in pursuit of regional, international and national goals separate from their stated desires to eliminate the armed group. The UN stabilization mission in Congo's (MONUSCO) understanding of the ADF was influenced by organizational limitations and the shortcomings of individual analysts, producing flawed assessments and ineffective policy decisions. Indeed, the many 'faces' of ADF tell us more about the ADF's adversaries than they do about the rebels themselves. The article shows how the policies towards the ADF may not be directly related to defeating a rebel threat, but rather enable the framers (e.g. DRC and Ugandan governments) to pursue various political and economic objectives, or lead the framers to pursue misguided operational plans (e.g. MONUSCO). In doing so, the article highlights more broadly the importance of the production of knowledge on conflicts and rebel groups: the way in which a rebel group is instrumentalized, or in which organizational structure impact on the understanding of the rebel group, are crucial not only in understanding the context, but also in understanding the interventions on the ground. [ABSTRACT FROM AUTHOR]

Time between vulnerability disclosures: A measure of software product vulnerability

Time between vulnerability disclosure (TBVD) for individual analysts is proposed as a meaningful measure of the likelihood of finding a zero-day vulnerability within a given timeframe. Based on publicly available data, probabilistic estimates of the TBVD of various software products are provided.Sixty-nine thousand six hundred forty-six vulnerabilities from the National Vulnerability Database (NVD) and the SecurityFocus Vulnerability Database were harvested, integrated and categorized according to the analysts responsible for their disclosure as well as by the affected software products. Probability distributions were fitted to the TBVD per analyst and product. Among competing distributions, the Gamma distribution demonstrated the best fit, with the shape parameter, k, similar for most products and analysts, while the scale parameter, θ, differed significantly. For forecasting, autoregressive models of the first order were fitted to the TBVD time series for various products. Evaluation demonstrated that forecasting of TBVD on a per product basis was feasible. Products were also characterized by their relative susceptibility to vulnerabilities with impact on confidentiality, integrity and availability respectively.The differences in TBVD between products is significant, e.g. spanning differences of over 500% among the 20 most common software products in our data. Differences are further accentuated by the differing impact, so that, e.g., the mean working time between disclosure of vulnerabilities with a complete impact on integrity (as defined by the Common Vulnerability Scoring System) for Linux (110 days) exceeds that of Windows 7 (6 days) by over 18 times.

Does Common Analyst Coverage Explain Excess Comovement?

This article shows that correlated errors in news about fundamentals are an important, rational determinant of excess comovement. Individual analysts’ forecast errors tend to be correlated across stocks. Using a proxy for correlated forecast errors based on analyst coverage, I find that stocks with similar sets of analysts exhibit more excess comovement, controlling for industry and other variables. Exogenous changes in commonality in analyst coverage around i) brokerage firm mergers and ii) additions to an index lead to changes in excess comovement. This information channel explains 10% to 25% of the increase in comovement around additions to the S&P 500 index.

Changes in the I/B/E/S Database and Their Effect on the Observed Properties of Analyst Forecasts

I/B/E/S is a common source of analyst forecast data, and the consistency of the data is important for both practice and academic research. We document substantial changes in the historical contents of I/B/E/S over time. We compare two versions of the I/B/E/S EPS detail file: one available in 2009 and the other available in 2015. Examining a common sample period (1993 through 2007), we find that 12 percent of the observations in the earlier version of I/B/E/S are omitted from the later version, whereas 6 percent are added to the later version as new observations that did not exist in the earlier version, and 9 percent of the observations in both versions report different forecasts of earnings. These discrepancies are systematically associated with various firm and analyst characteristics. We find that earnings forecasts in the later version are significantly more accurate, less biased, and bolder than the forecasts in the earlier version. We also find substantial differences in the firms identified as meeting or just beating analysts’ expectations of earnings. Finally, these discrepancies are less pronounced when comparing separate versions of the EPS summary file, but are more pronounced when examining individual analyst quarterly forecasts and annual cash flow forecasts.

Range Has it: Decoding the Information Content of Forecast Ranges

Given that most management forecasts in recent years are issued in range form and that most firms are followed by multiple analysts issuing dispersed forecasts, the conventional measure of management forecast news based on the midpoint of management forecast and the consensus analyst forecast does not fully capture the information content conveyed by range forecasts. This paper seeks to partially uncover such information by investigating an unexamined dimension unique to range forecasts –– overlapping between management forecast ranges and the ranges formed by individual analyst forecasts, which occurs in nearly 84% of range forecasts. Controlling for the width of management forecasts and analyst forecast dispersion, we find that compared with “overlap” management forecasts, “non-overlap” forecasts have greater impact on analyst revisions and stock market reaction to per unit forecast news. The non-overlap forecasts also turn out to have high relative forecast accuracy over analyst consensus forecasts. Moreover, individual analysts whose prior forecasts are outside of management forecast ranges respond more strongly to “non-overlap” forecasts than to “overlap” forecasts, and this difference is significant only when management forecasts convey bad news. Overall, our findings suggest that range forecasts convey more information beyond just the midpoint and analysts’ consensus.

Influence of scenario choices when performing CFD simulations for explosion risk analyses: Focus on dispersion

Explosion risk analyses (ERAs) are widely used as means for deriving the dimensioning accidental loads (DALs) for design of offshore topside facilities. ERAs can predict explosion loads in detail, including overpressures, differential pressure, and drag loads (FABIG TN-08). Loads with returning frequencies of 1E-04 per year are commonly adopted as DALs and are effectively incorporated in standards and legislation such as NORSOK Z013 (NORSOK, 2010) and ISO 19901-3 (ISO, 2010). Detailed guidelines for how to perform these analyses are described in the Norwegian standard NORSOK Z013. Computational fluid dynamic (CFD) simulations form a key part of these analyses and how these simulations are performed, influence the accuracy and uncertainty of the predicted loads. Dispersion simulations are a particular important part of these studies as they both define the size of the clouds and give basis for detailed ignition probability modeling. This paper explores how explosion risk and the dimensioning accidental loads are affected by dispersion scenario choices and address the associated uncertainty. This research work is conducted as a part of an explosion risk analysis for a large process area of an FPSO. This study involved extensive CFD efforts with more than 3000 CFD simulations minimizing the use of simplifications. Subsequent steps involved testing various means of simplification and also an assessment of how the choices made by individual analysts could influence the results.

Where the Rubber Meets the Road

Sociologists use data from experiments, ethnographies, survey interviews, in-depth interviews, archives, and administrative records. Analysts disagree, however, on whether probability sampling is necessary for each method. To address the issue, the author introduces eight dimensions of data collection, places each method within those dimensions, and uses that resource to assess the necessity and feasibility of probability sampling for each method. The author finds that some methods often seen as unique are not, whereas others’ unique natures are confirmed. More surprisingly, some methods for which probability sampling is rare were found to require it, whereas one for which probability sampling is usually believed to be impossible was found to easily use it. Efforts to salvage nonprobability samples and eight additional general justifications for nonprobability sampling are addressed. Advice for individual analysts and counsel for collective responses to improve research are offered.

Understanding Rankings of Financial Analysts

The prediction of the most accurate analysts is typically modeled in terms of individual analyst characteristics. This approach has the disadvantage that these data are hard to collect and often unreliable. We follow a different approach in which we characterize the general behavior of rankings of analysts based upon state variables rather than individual analyst characteristics or past accuracy. We use a common learning algorithm, naive Bayes, that we adapted to address the problem of ranking the analysts. Our results show that it is possible to model the relation between the selected variables and the rankings. We show that the uncertainty about future stock performance influences the rankings of the analysts while the macroeconomic variables have the most contribution to the changes in rankings.

Research and Development, Uncertainty, and Analysts’ Forecasts: The Case of IAS 38

AbstractThis study analyzes the consequences of the capitalization of development expenditures under IAS 38 on analysts’ earnings forecasts. We use unique hand‐collected data in a sample of highly research and development (R&D)‐intensive German‐listed firms over the period 2000–2007. We find that the capitalization of development costs is significantly associated with both higher individual analysts’ forecast errors and forecast dispersion. This suggests that the increasing complexity surrounding the capitalization of development costs negatively impacts forecast accuracy. However, for firms with high underlying environmental uncertainty, forecast errors are negatively associated with capitalized development expenditures. This indicates that the negative impact of increased complexity on forecast accuracy can be outweighed by the information contained in the signals from capitalized development costs when the underlying environmental uncertainty is high. The findings contribute to the ongoing controversial debate on the accounting for self‐generated intangible assets. Our results provide useful insights on the link between capitalization of development costs, environmental uncertainty, and analysts’ forecasts for accounting academics and practitioners alike.

Reassessment of Fixed Assets

Making the economic decisions depend to a large extent on the quality and level of information that owns. The quality and the reliable information obtained from financial statements is very important for successful decision-making and the economic development of a country, for the development of financial markets, to promote the entry of foreign investments and economic stability of the country. The financial statements are prepared in accordance with International Financial Reporting Standards (IFRS) published for the first time by the Board of the International Accounting Standards. This study aims to present information related to the valuation of assets by taking as a concrete case Durres Port Authority. Risk assessment and return of assets is the basis of quantitative distribution of assets and portfolio risk management. The economists can calculated these parameters through the observed price. These awards provide information on the value of the asset, based on valuation methods and individual analysts' expectations. In this content, to continue with the results, we need to understand how we can asses individual titles in accordinace with the market. DOI: 10.5901/mjss.2015.v6n5p107

Consistent Analyst Expectation Error and Earnings Management: Evidence from China

ABSTRACTUsing data from the Chinese A-share market in 2004–12, we show how cognitive bias of individual analysts led to counterproductive effect in less-developed financial markets. We form an ex ante measure of analysts’ expectation error, a measure suitable for markets with a short history. We find that star analysts tend to be more optimistic than ordinary analysts, and their biased opinions influence other analysts because of analyst herding behavior. Two-stage least square regression results suggest that consistent expectation errors among analysts can lead to earnings management. These insights are valuable to investors and regulators.

Identification of refined petroleum products in contaminated soils using an identification index for GC chromatograms.

Hydrocarbons found in the environment are typically characterized by gas chromatography (GC). The shape of the GC chromatogram has been used to identify the source of petroleum contamination. However, the conventional practice of simply comparing the peak patterns of source products to those of environmental samples is dependent on the subjective decisions of individual analysts. We have developed and verified a quantitative analytical method for interpreting GC chromatograms to distinguish refined petroleum products in contaminated soils. We found that chromatograms for gasoline, kerosene, and diesel could be divided into three ranges with boundaries at C6, C8, C16, and C26. In addition, the relative peak area (RPA(GC)) of each range, a dimensionless ratio of the peak area within each range to that of the total range (C6-C26), had a unique value for each petroleum product. An identification index for GC chromatograms (ID(GC)), defined as the ratio of RPA(GC) of C8-C16 to that of C16-C26, was able to identify diesel and kerosene sources in samples extracted from artificially contaminated soils even after weathering. Thus, the ID(GC) can be used to effectively distinguish between refined petroleum products in contaminated soils.

A Theory of Music Analysis: On Segmentation and Associative Organization by Dora A. Hanninen (review)

Reviewed by: A Theory of Music Analysis: On Segmentation and Associative Organization by Dora A. Hanninen Dimitris Exarchos A Theory of Music Analysis: On Segmentation and Associative Organization. By Dora A. Hanninen . ( Eastman Studies in Music, vol. 92 .) Rochester, NY : University of Rochester Press , 2012 . [ xii, 530 p. ISBN 9781580461948 . $82 .] Music examples, illustrations, glossary, bibliography, index. Comprising research spanning over a decade, A Theory of Music Analysis constitutes a comprehensive account and a culmination of Dora A. Hanninen’s work to date. To the extent that no theory of music is independent of analysis, this work is indispensable as a theory of analysis. The main (philosophical) concern of the book is how to create a precise analytical language, one that can secure a credible interpretation; intertwined with this is a (practical) concern with how to employ this language in a way that reflects the individual analyst’s flair. Analytical applications are not set against theory as singular instances of ready-made methods; on the contrary, by negating the usual rhetoric of theory and analysis, Hanninen sets out to provide a kind of metatheory, independent of the particular existing music-theoretic tools that it aims to encompass. This level of interaction between Hanninen’s overarching metatheory (or simply “theory”) and particular, or what she calls orienting theories is, I think, most intriguing and—although at times not easy to ascertain—promising. From the outset, the author wishes to safeguard the “interpretive autonomy and imagination of the individual analysts” who use the theory (p. 4). In this spirit, the book addresses music theorists, analysts, and musicologists. It is ideal for a postgraduate and academic readership and it could serve as a main reference item in analysis courses. Analysis scholars will appreciate its focus on segmentation. From atonal-theoretic to semiotic methods, segmentation relies on interpretation of the music score. Hanninen endeavors to formalize segmentation protocols by constructing three basic types of criteria; although she provides comprehensive lists for the three types, these lists are left open-ended. The book encompasses a wide range of Western music traditions from the baroque onward. This feature is one among its several qualities of methodological flexibility, afforded precisely by the relation between metatheory and orienting theory. From Bach to Brahms and from Varèse to Babbitt, the first half of the book abounds in examples and indicative applications of analytical tools and concepts. The second half comprises a set of six detailed analyses of mostly piano-based music, two European (Beethoven and Debussy), and four American (Nancarrow, Riley, Feldman, and Morris). Analytically, the book employs, and thus presupposes, a firm knowledge of common-practice tonality, serial systems, atonal theory, and Schenkerian analysis. These four approaches suffice to cover the repertoire with which Hanninen engages. Having said that, the flexibility just mentioned should provide enough space for one to apply new orienting theories to analysis (e.g., the often-idiosyncratic approaches by Iannis Xenakis, who is mentioned briefly). Orientations and criteria for segmentation form the theory’s conceptual part, while associative sets and organization lie on the objective end, segments being the interface. The schematic of the theory is a synoptic representation of these five levels, which interact within three domains: sonic (psychoacoustic), contextual (associative), structural (theoretical). Thus, orientations refer respectively to disjunction, association, theory; and criteria can be sonic, contextual, structural, or linked as structural–sonic, structural–contextual. Any analyst of the post-tonal repertoire will appreciate the difficulties around segmentation strategies. Allen Forte’s distinction between primary and composite segments, or the practice of imbrication, can be useful, but his reference to contextual criteria for segmentation was left un-systematized. In practice, this was counter-balanced by a refining of the segmentation process according to both those contextual criteria and set relations (The Structure of [End Page 320] Atonal Music [New Haven, CT: Yale University Press, 1973], 91). Such practices point to a conundrum of theory and analysis: to what extent should we allow (the orienting) theory to affect our interpretation? Such problematics are usually confronted in analysis classes, where students are required to provide a convincing (as opposed to convenient) segmentation. The three types of criteria that Hanninen’s theory provides refer...

Why Do Financial Analysts Strive to Be Irrelevant? Career Concerns and Endogenous Coverage Termination

In this paper we investigate the negative relationship between analysts’ coverage and stocks idiosyncratic volatility. While prior research argues that analysts cause the low level of idiosyncratic risk because they lack access to firm-specific information we hypothesize that the causal relation goes in the opposite direction. Specifically we argue that due to reputational concerns analysts avoid covering stocks with high levels of specific risk. Using three novel quasi-natural experiments we show that analysts’ coverage drops after an exogenous increase in idiosyncratic volatility. We also show that coverage increases after an exogenous decrease of specific risk and that an exogenous decrease in the number of analysts covering a firm lead to a decrease firm-specific information. Looking at individual analysts’ characteristics we show that the probability of dropping coverage after an increase in idiosyncratic risk is higher for younger analysts, analysts with lower reputation and lower risk aversion.

How Analysts Process Information: Technical and Financial Disclosures in the Microprocessor Industry

Following Bradshaw (‘Analyst information processing, financial regulation, and academic research’ [2009], and Analysts' forecasts: What do we know after decades of work? [2011]), this paper examines how analysts process information, particularly in an information environment characterised by multiple and potentially complementary information sources. The setting is the microprocessor industry, one in which technical information is particularly significant and complex to digest. Based on 3837 analyst earnings-forecast revisions, issued by 134 analysts, we examine quantitatively the speed, magnitude, and information content of the reactions of individual analysts and subgroups of analysts to both periodic and timely technical disclosures, and as a complement to periodic financial disclosure. We find that analysts are much slower to react to timely technical disclosures than they are to periodic financial disclosures. We find also that technical and financial disclosures complement each other. Furthermore, we find that there is a ‘hierarchy’ of analysts in this particular industry, as evidenced through the strength of reaction to timely technical disclosures. Finally, we find that lower speed in reacting to timely technical disclosures and a higher intensity in the use of timely technical disclosure (in conjunction with periodic financial disclosure) result in greater accuracy, and that more experienced analysts tend to be less accurate. We suggest that the findings may have implications for other industries such as Bio-Tech Pharma.

Meeting Individual Analyst Expectations

ABSTRACTThe expectations management literature has so far focused on firms meeting the analyst consensus forecast—the expectations of analysts as a group—at earnings announcements. In this study we argue that investors may use individual analyst forecasts as additional benchmarks in evaluating reported earnings because the consensus forecast underutilizes private information contained in individual analyst forecasts. We predict that measures reflecting such private information have incremental explanatory power over the consensus forecast for the market's reaction to earnings news. We find results consistent with this prediction by examining two measures: (1) the percentage of individual forecasts met and (2) meeting the key analyst forecast. We extend the literature by documenting the role of individual analyst forecasts in investors' evaluations of reported earnings.JEL Classifications: G10; G11; G17; G14; G24.Data Availability: Data are publicly available from the sources identified in the paper.