Analysts Face Research Articles

A Level Playing Field? Empirical Evidence That Ethnic Minority Analysts Face Unequal Access to Corporate Managers

We investigate whether ethnic minority analysts face unique barriers to management access. We find managers are less likely to select minority analysts to participate in the Q&A session of public earnings conference calls, and minority analysts selected to participate receive lower levels of prioritization and engagement than nonminority analysts. Minority analysts’ unequal access to management persists over time, and their access does not improve with companies recognized for workplace diversity. However, we find some evidence that diverse executive teams are associated with more favorable participation outcomes for minority analysts. The consequences of unequal treatment extend beyond conference calls, as investors are less likely to vote for minority analysts as Institutional Investor All-Stars.

Measuring sustainability risks: A rational myth?

AbstractThe objective of this paper is to investigate the rigorousness and reliability of sustainability rating agencies' evaluation of corporate sustainability risks. Using grounded theory, this study conducts a qualitative analysis of 32 semi‐structured interviews with practitioners involved in this activity and shows the trade‐offs and rational myths underlying this evaluation process. The image of rationality and rigorousness projected by sustainability risk measurements is mostly intended to address the increasing institutional pressures for reliable and comparable information, particularly from institutional investors and socially responsible investment decision makers. Nevertheless, risk analysts face serious challenges due to the lack of reliable information, the unpredictability of sustainability risks, the methodological issues related to the measurement process, and the complexity and context‐dependency of risk assessment. These challenges call into question the official and optimistic rhetoric of rating agencies. This study contributes to the literature on sustainability risks and rational myths in organizations. Managerial implications and avenues for future research are also discussed.

Busy Analysts and Firm Monitoring

Consistent with the view that “busy” analysts face time and effort constraints in monitoring firms, we find that higher busyness lowers firm valuation. The underlying mechanisms include lower operating performance, higher cost of capital, greater earnings management, excessive CEO compensation, and lower institutional ownership. These effects are less pronounced for larger firms and when analysts have greater experience covering the firm/industry. Our inferences are supported by a novel experimental design based on shocks to analyst busyness from broker mergers. These results suggest that analyst monitoring relies not only on the level of analyst coverage, but also on its quality.

An Initial Evaluation of an Assessment Method for the PEAK Relational System Direct Training Module.

When considering the best available treatment, it is crucial that assessments yield valid and reliable measures to produce effective treatment options. Currently, this research is limited, giving behavior analysis a platform to evaluate the psychometric properties and content validity of assessment tools used across settings. One major issue that practicing behavior analysts face is conducting the most comprehensive assessment within the time constraints put in place by insurance companies. Utilizing a method of assessment that includes indirect and direct descriptive methods and experimental manipulations could aid in cutting down assessment time, especially if those methods have known correspondence with each other. The purpose of the present study was to assess the components of the Promoting the Emergence of Advanced Knowledge Relational Training System: Direct Training Module (PEAK-DT) for children with autism. More specifically, this study evaluated the correspondence between the PEAK indirect assessment (PEAK-IA) and PEAK preassessment (PEAK-PA) for the Direct Training Module. Comparisons were also made to determine which method offers the best predictive validity of actual performance on the PEAK-DT module. Results indicate that PEAK-IA completed by parents and PEAK-PA share moderate correspondence, with the PEAK-PA offering the strongest predictive validity of direct testing outcomes. Implications for behavior-analytic practice, as well as directions for future research, are discussed.

Diversified Firms and Analyst Earnings Forecasts: The Role of Management Guidance at the Segment Level

ABSTRACTUsing a unique, manually collected dataset, we are the first to analyze the role that management guidance at the segment level plays for the financial analyst earnings forecasts of diversified firms. About half of the diversified European firms in the sample provide segment-level guidance (SLG), with considerable variation in precision and disaggregation. We find that (1) analyst earnings forecast errors are smaller, and (2) the magnitude of disagreement between individual forecasts and the average forecast is lower for firms that provide SLG, beyond the effect of group-level guidance. The results hold in matched samples and within-firm analyses around SLG initiation. We further show that the results are stronger in situations characterized by higher information asymmetry, but not in situations characterized by operational complexity. Overall, the results imply that SLG mitigates, to some extent, the difficult task that financial analysts face when valuing diversified companies.

Concurrent Earnings Announcements and Analysts' Information Production

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.

Interactive Visual Analytics for Sensemaking with Big Text

Abstract Analysts face many steep challenges when performing sensemaking tasks on collections of textual information larger than can be reasonably analyzed without computational assistance. To scale up such sensemaking tasks, new methods are needed to interactively integrate human cognitive sensemaking activity with machine learning. Towards that goal, we offer a human-in-the-loop computational model that mirrors the human sensemaking process, and consists of foraging and synthesis sub-processes. We model the synthesis loop as an interactive spatial projection and the foraging loop as an interactive relevance ranking combined with topic modeling. We combine these two components of the sensemaking process using semantic interaction such that the human's spatial synthesis actions are transformed into automated foraging and synthesis of new relevant information. Ultimately, the model's ability to forage as a result of the analyst's synthesis activities makes interacting with big text data easier and more efficient, thereby facilitating analysts' sensemaking ability. We discuss the interaction design and theory behind our interactive sensemaking model. The model is embodied in a novel visual analytics prototype called Cosmos in which analysts synthesize structure within the larger corpus by directly interacting with a reduced-dimensionality space to express relationships on a subset of data. We then demonstrate how Cosmos supports sensemaking tasks with a realistic scenario that investigates the affect of natural disasters in Adelaide, Australia in September 2016 using a database of over 30,000 news articles.

Diversified Firms and Analysts' Earnings Forecasts: The Role of Management Guidance at the Segment Level

Using a unique, manually-collected dataset, we are the first to analyze the role that management guidance at the segment level plays for the financial analyst earnings forecasts of diversified firms. About half of the diversified European firms in the sample provide segment-level guidance (SLG), with considerable variation in precision and disaggregation. We find that (1) analyst earnings forecast errors are smaller, and (2) the magnitude of disagreement between individual forecasts and the average forecast is lower for firms that provide SLG, beyond the effect of group-level guidance. The results hold in matched samples and within-firm analyses around SLG initiation. We further show that the results are stronger in situations characterized by higher information asymmetry, but not in situations characterized by operational complexity. Overall, the results imply that SLG mitigates to some extent the difficult task that financial analysts face when valuing diversified companies.

‘The Stone Mother Revisited’: A female group analytic reflection on working within male low-secure forensic inpatient settings in the United Kingdom

This reflective account offers a female group analytic perspective on working with male inpatients within low-secure forensic settings. Viewed through the metaphor ‘Stone Mother’, I suggest that this setting is especially challenging for female group analysts because of its paternalistic matrix and the unflattering portrayal of women as ‘seductive temptress’ or ‘evil stepmothers’ in our inherited cultural foundation matrix. Females and female bodies therefore evoke distinct transferences and projections. In the sexual and gendered matrix of violence on the forensic male ward, these may be particularly malignant due to the aetiology and perpetuation of violence in a male-dominated world of power relations. Clinical vignettes are presented to demonstrate the many challenges female group analysts face in this setting. These are analysed through the lens of group analytic concepts and practice. Gender expectations are problematized. I shall suggest that therapy groups can instil a sense of belonging, acting as anti-shaming strategy and make a difference. However, due to the complexity of the sexual and gendered dynamic matrix of violence, violence cannot be treated in isolation on forensic wards by a predominantly female workforce. It must be addressed globally through dialogues in communities and beyond. Group analysis could take on this challenge.

Incorporating macroeconomic leading indicators in tactical capacity planning

Tactical capacity planning relies on future estimates of demand for the mid- to long-term. On these forecast horizons there is increased uncertainty that the analysts face. To this purpose, we incorporate macroeconomic variables into microeconomic demand forecasting. Forecast accuracy metrics, which are typically used to assess improvements in predictions, are proxies of the real decision associated costs. However, measuring the direct impact on decisions is preferable. In this paper, we examine the capacity planning decision at plant level of a manufacturer. Through an inventory simulation setup, we evaluate the gains of incorporating external macroeconomic information in the forecasts, directly, in terms of achieving target service levels and inventory performance. Furthermore, we provide an approach to indicate capacity alerts, which can serve as input for global capacity pooling decisions. Our work has two main contributions. First, we demonstrate the added value of leading indicator information in forecasting models, when evaluated directly on capacity planning. Second, we provide additional evidence that traditional metrics of forecast accuracy exhibit weak connection with the real decision costs, in particular for capacity planning. We propose a more realistic assessment of the forecast quality by evaluating both the first and second moment of the forecast distribution. We discuss implications for practice, in particular given the typical over-reliance on forecast accuracy metrics for choosing the appropriate forecasting model.

Integration of Heterogeneous Requirements using Ontologies

Ontology-driven approaches are used to sustain the requirement engineering process. Ontologies can be used to define information and knowledge semantics during the requirements engineering phases, such as analysis, specification, validation and management of requirements. However, requirement analysts face difficulties in using ontologies for requirement engineering. In this study, a framework has been proposed to integrate heterogeneous requirements by using local and global ontologies.

Heaping on Dividends: The Role of Dividend Size and Information Uncertainty

In this paper we contribute to a line of literature that examines behavioral biases that impact important corporate decisions. Our paper builds on prior articles that examine heaping or rounding of EPS forecasts. Herrmann and Thomas (2005), Bamber, Hui and Yeung (2010) and Dechow and You (2012) observe that firm managers and analysts systematically round EPS forecasts. They also find more rounding for EPS forecasts with larger magnitude, and when management or analysts face higher levels of information uncertainty. In this study we examine whether dividend policy is also affected by rounding and the heaping heuristic. The previous literature that examines heaping related to corporate earnings focuses on heaping in earnings forecasts, however our study focuses on heaping in actual distributed dividends that directly impact key firm characteristics such as firm capital structure, investor returns and overall firm performance. From 1926 through 2015 we report 51.40% of dividends are heaped. In the earlier years, dividends are almost always heaped, but heaping declines through time. In some years 100% of quarterly dividends are heaped. We find heaping is significantly related to dividend size, and the level of information uncertainty faced by firm management. Of the uncertainty metrics, inverted firm size has the strongest relation with the likelihood of heaping. We also observe that heaped dividends are altered less frequently, and when there is a dividend change to a previously heaped dividend it is more often an increase or decrease to another heaped amount.

Finding Control Cases and Maintaining Immersion: Challenges and Opportunities.

Given that surveys, as well as frequent observations by institute faculty, indicate that many candidates have difficulty finding control cases and maintaining immersion and that many graduate analysts face similar challenges, it would seem that psychoanalytic training does not prepare candidates adequately for finding patients and practicing analysis while in training and, for many, after they have graduated. Although external challenges are formidable, it is by identifying and making use of internal challenges to finding cases that candidates can develop an analytic mind: the identity, approach, and skills necessary not only to graduate but to have the choice to practice clinical psychoanalysis post-graduation. Some of the internal challenges and their manifestations in different phases of initiating analysis (referrals, initial consultation, recommendation) are discussed and two detailed examples are offered to illustrate the productive use of candidates' countertransferences in finding cases and maintaining immersion. Finally, recommendations for institutional solutions are provided.

Earnings Announcement Clustering and Analyst Forecast Behavior

We examine how a limited attention effect can directly affect the information processing of a sophisticated market participant. Specifically, we investigate whether analysts respond to earnings announcement events differently if more than one firm in their coverage portfolios announce earnings on the same day (“Busy Analysts”). We find evidence that, for the same firm, analysts delay or even skip issuing earnings forecasts in the quarters when they are busy versus quarters when they are not. Compared with “Not-busy Analysts”, the initial responses of Busy Analysts are of lower forecast quality (in terms of forecast accuracy improvements, forecast boldness, and the price impact of forecasts). Further, we find that when analysts face multiple concurrent earnings announcements, they tend to prioritize issuing forecasts for firms with richer information environments over firms with weaker information environments. At the firm level, when a larger percentage of analysts covering the firm are busy, the analysts as a whole are less responsive to the firm’s earnings announcements, implying that the price discovery after the information event is slower. Our study extends the literatures of limited attention and analysts’ information production.

Financial Analysts' Processing of Financial Reporting Information in Emerging and Frontier Markets

Objective: The purpose is to establish the relationship between the analysts' stock valuation, the reporting environment and the stock recommendations. And to investigate the process of incorporating both quantitative and qualitative information into their forecasts.
 Methodology: The research will be archival, obtaining historical information from DataStream, Stock Exchanges and MSCI. A survey will also be carried out to corroborate findings, as the analysts' valuation process may not be obtained through desk research.
 Results: The results are expected to show evidence for the incentive conflicts of the analysts' decisions with regards to the peculiarities of the macroeconomic environments under consideration.
 Implication: The write up has implications for sell-side analysts where they have perceived incentive conflicts when they make recommendations. The research contributes to the argument on the conflicts of interest analysts face in forecasting earnings and making recommendations within the markets peculiar environment.

CEMAP II: An Architecture and Specifications to Facilitate the Importing of Real-World Data into the CASOS Software Suite

Abstract : An often overbearing logistical problem that social network researchers and analysts face is the challenge of carrying out the basic data processing steps necessary to transform real-world source data into a form that is formatted specifically to the requirements of particular social network analysis software. In particular, this paper focuses on resolving the variety of real-world data forms that must be transformed into the format necessities of the software suite developed at the Center for Computational Analysis of Social and Organizational Systems (CASOS) at Carnegie Mellon University. This data processing or programming problem, while rather straightforward, can be daunting to non-I.T. users of these -- and any -- software tools, and at best case, laboriously costly for the programming savvy. This report outlines and describes a software architecture and user paradigm to address this ubiquitous problem. This report provides a basis for later detailed design and implementation of the ideas put forth in this report which will ultimately results in tangible features being made available and fully operational for users of the CASOS software suite (ORA, AutoMap, and Construct).

How do analysts interpret management range forecasts?

Range forecasts have evolved to be the most common form of management forecasts. Prior studies typically use the midpoint to evaluate analyst reaction to range forecasts, implicitly assuming that analysts place equal weights on the upper and the lower bounds of management range forecasts. We empirically test this restrictive assumption and provide strong evidence of unequal weights – analysts place significantly more (less) weight on the lower (upper) bound of forecast ranges. Moreover, such overweight on the lower bound is more pronounced when analysts face higher ambiguity, consistent with the “max–min” axiom, which predicts that decision-makers tend to assign higher probability to the worst-case scenario when facing ambiguity. Further tests show that “optimal revisions” with perfect foresight of actual earnings also overweight the lower bound.

Deadline scheduling for aperiodic tasks in inter-Cloud environments: a new approach to resource management

In the big data era, the speed of analytical processing is influenced by the storage and retrieval capabilities to handle large amounts of data. While the distributed crunching applications themselves can yield useful information, the analysts face difficult challenges: they need to predict how much data to process and where, such that to get an optimum data crunching cost, while also respect deadlines and service level agreements within a limited budget. In today's data centers, data processing on demand and data transfers requests coming from distributed applications are usually expressed as aperiodic tasks. In this paper, we challenge the problem of tasks scheduling with deadline constraints of aperiodic tasks within inter-Cloud environments. In massively multithreaded computing systems that deal with data-intensive applications, Hadoop and BaTs tasks arrive periodically, which challenges traditional scheduling approaches previously proposed for supercomputing. Here, we consider the deadline as the main constraint, and propose a method to estimate the number of resources needed to schedule a set of aperiodic tasks, considering both execution and data transfers costs. Starting from classical scheduling techniques, and considering asynchronous tasks handling, we analyze the possibility of decoupling task arriving from task creation, scheduling and execution, sets of actions that can be put into a peer-to-peer relation over a network or over a client---server architecture in the Cloud. Based on a mathematical model, and using different simulation scenarios, we prove the following statements: (1) multiple source of independent aperiodic tasks can be considered similar to a single one; (2) with respect to the global deadline, the tasks migration between different regional centers is the appropriate solution when the number of estimated resources exceed a data center capacity; and (3) in a heterogeneous data center, we need a higher number of resources for the same request in order to respect the deadline constraints. We believe such results will benefit researchers and practitioners alike, who are interested in optimizing the resource management in data centers according to novel challenges coming from next-generation big data applications.

How Do Analysts Interpret Management Range Forecasts?

Range forecasts have evolved to be the most common form of management forecasts. Prior studies typically use the midpoint to evaluate analyst reaction to range forecasts, implicitly assuming that analysts place equal weights on the upper and the lower bounds of management range forecasts. We empirically test this restrictive assumption and provide strong evidence of unequal weights ─ analysts place significantly more (less) weight on the lower (upper) bound of forecast ranges. Moreover, such overweight on the lower bound is more pronounced when analysts face higher ambiguity, consistent with the “max-min” axiom, which predicts that decision-makers tend to assign higher probability to the worst-case scenario when facing ambiguity. Further tests show that “optimal revisions” with perfect foresight of actual earnings also overweight the lower bound.

A Process to Support Analysts in Exploring and Selecting Content from Online Forums

The public content increasingly available on the Internet, especially in online forums, enables researchers to study society in new ways. However, qualitative analysis of online forums is very time consuming and most content is not related to researchers’ interest. Consequently, analysts face the following problem: how to efficiently explore and select the content to be analyzed? This article introduces a new process to support analysts in solving this problem. This process is based on unsupervised machine learning techniques like hierarchical clustering and term co-occurrence network. A tool that helps to apply the proposed process was created to provide consolidated and structured results. This includes measurements and a content exploration interface.