Analyst Expertise Research Articles

How does analysts' forecast quality relate to corporate investment efficiency?

We examine the impact of financial analysts on the efficiency of firms' investment decisions. We use the accuracy and dispersion of financial analysts' earnings forecasts as proxies of analyst expertise and quality in making forecasts. We find that high quality forecast is associated with higher investment if the firm is more likely to under-invest and lower investment if the firm is more likely to over-invest, suggesting that forecast quality increases firm-level investment efficiency. We further show that such effects are stronger for the firms with higher information asymmetry and lower institutional stock ownership. The results are consistent with the notion that higher quality of analyst forecasts increases the information environment and external monitoring, which in turn increases investment efficiency.

Dynamic Scheduling of Cybersecurity Analysts for Minimizing Risk Using Reinforcement Learning

An important component of the cyber-defense mechanism is the adequate staffing levels of its cybersecurity analyst workforce and their optimal assignment to sensors for investigating the dynamic alert traffic. The ever-increasing cybersecurity threats faced by today’s digital systems require a strong cyber-defense mechanism that is both reactive in its response to mitigate the known risk and proactive in being prepared for handling the unknown risks. In order to be proactive for handling the unknown risks, the above workforce must be scheduled dynamically so the system is adaptive to meet the day-to-day stochastic demands on its workforce (both size and expertise mix). The stochastic demands on the workforce stem from the varying alert generation and their significance rate, which causes an uncertainty for the cybersecurity analyst scheduler that is attempting to schedule analysts for work and allocate sensors to analysts. Sensor data are analyzed by automatic processing systems, and alerts are generated. A portion of these alerts is categorized to be significant , which requires thorough examination by a cybersecurity analyst. Risk, in this article, is defined as the percentage of significant alerts that are not thoroughly analyzed by analysts. In order to minimize risk, it is imperative that the cyber-defense system accurately estimates the future significant alert generation rate and dynamically schedules its workforce to meet the stochastic workload demand to analyze them. The article presents a reinforcement learning-based stochastic dynamic programming optimization model that incorporates the above estimates of future alert rates and responds by dynamically scheduling cybersecurity analysts to minimize risk (i.e., maximize significant alert coverage by analysts) and maintain the risk under a pre-determined upper bound. The article tests the dynamic optimization model and compares the results to an integer programming model that optimizes the static staffing needs based on a daily-average alert generation rate with no estimation of future alert rates (static workforce model). Results indicate that over a finite planning horizon, the learning-based optimization model, through a dynamic (on-call) workforce in addition to the static workforce, (a) is capable of balancing risk between days and reducing overall risk better than the static model, (b) is scalable and capable of identifying the quantity and the right mix of analyst expertise in an organization, and (c) is able to determine their dynamic (on-call) schedule and their sensor-to-analyst allocation in order to maintain risk below a given upper bound. Several meta-principles are presented, which are derived from the optimization model, and they further serve as guiding principles for hiring and scheduling cybersecurity analysts. Days-off scheduling was performed to determine analyst weekly work schedules that met the cybersecurity system’s workforce constraints and requirements.

Conservatism-Matching Analysts and Their Performance

This study investigates whether analysts can benefit from understanding a target firm’s conditional conservatism. In particular, we examine the forecasting performance of analysts when they match the asymmetric timeliness of their earnings forecast revisions (i.e., forecast conservatism) with the asymmetric timeliness of firms’ reported earnings (i.e., accounting conservatism). We find that better conservatism-matching analysts produce more accurate earnings forecasts and elicit stronger market reactions to their forecast revisions. Further, better conservatism-matching analysts issue more profitable stock recommendations and have more favorable career outcomes. Our results indicate that analysts’ ability to understand accounting conservatism is an important reflection of analyst expertise and professional success.

Analyst Quality, Optimistic Bias, and Reactions to Major News

We investigate whether financial analysts' characteristics are associated with the asymmetric response of stock recommendations between positive and negative information shocks. We hypothesize that incentives exist such that the analyst's experience, expertise, reputation, and career concerns are positively associated with the timely revelation of negative news about a firm. As a result, we expect the asymmetric response to be reduced for superior analysts. Using the stock return/recommendation changes relation, we find that the asymmetric reaction is inversely associated with analysts' characteristics that are indicative of higher quality. Further, the reduction is only present when analysts have negative private information. We therefore contribute to the research on differing analyst characteristics and report quality and provide additional insights on analyst bias.

An automatic model calibration method for occupant restraint systems

Computer models are widely used to simulate dynamic systems in automobile industry. It is imperative to have high quality CAE models with good predictive capability. This requires CAE engineers to conduct model calibration with physical tests. The challenges in the occupant restraint system model calibration are: (1) the dynamic system usually consists of multiple responses, (2) most of the responses are functional data or time histories, and (3) the traditional trial-and-error calibration approach is time consuming and highly depends on analyst's expertise. These call for the development of an automatic and effective model calibration method. This paper presents a newly developed automatic model calibration method, based on the Error Assessment of Response Time Histories (EARTH) metric. The EARTH metric is used to perform model assessment on various important features of the functional responses. A new multi-objective optimization problem is formulated and solved by a Non-dominated Sorting Genetic Algorithm to automatically update CAE model parameters. A real-world example is used to demonstrate the use of the proposed method.

Understanding analysts' use of stock returns and other analysts' revisions when forecasting earnings

We investigate analysts' use of stock returns and other analysts' forecast revisions in revising their own forecasts after an earnings announcement. We find that analysts respond more strongly to these signals when the signals are more informative about future earnings changes. Although analysts underreact to these signals on average, we find that analysts who are most sensitive to signal informativeness achieve superior forecast accuracy relative to their peers and have a greater influence on the market. The results suggest that the ability to extract information from the actions of others serves as one source of analyst expertise.

Space‐in‐Time and Time‐in‐Space Self‐Organizing Maps for Exploring Spatiotemporal Patterns

AbstractSpatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the “Self‐Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post‐processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two‐dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41‐years time series of 7 crime rate attributes in the states of the USA.

Quantitative Gel Electrophoresis: Sources of Variation

Gel electrophoresis is known for its often unsatisfactory precision. Percental relative standard deviations (RSD%) in a range of 15-70% have been reported. Therefore, an improvement of precision in quantitative 2-DE is necessary. In the present, study we have analyzed the work flow of 2-DE in detail to assess the main error sources. Potential major sources of variability for this technique include the transfer between first and second dimension, the analyst's expertise, and the staining or rather detection of separated proteins. The remarkable and completely irregular changes of the background signal from gel to gel were identified as one of the governing error sources. These background changes can be strongly reduced by the direct detection of the separated proteins using native fluorescence. More than a 3-fold better signal-to-noise ratio was found compared to Ruthenium-(II)-tris-(bathophenanthroline disulfonat) (RuBPS) and Coomassie staining, although the sample was used in an 800-fold lower concentration. This improvement together with well-defined peaks resulted in a better quantitative spot reproducibility of approximately 12-16% RSD%. Possibly, the variabilities due to detection and evaluation were already reduced to minor error components. However, according to the law of error propagation, the major error sources dominate the total error. To really prove the good detection and evaluation, these other sources of variability such as sample preparation, strip rehydration, protein loading, transfer between dimensions, interactions between gel and proteins, gel scanning, and spot integration have to be reduced next.

The financial analyst forecasting literature: A taxonomy with suggestions for further research

This paper develops a taxonomy of research examining the role of financial analysts in capital markets. The paper builds on the perspectives provided by Schipper [Schipper, K. (1991). Analysts' forecasts. Accounting Horizons, 5, 105–131] and Brown [Brown, L. (1993). Earnings forecasting research: Its implications for capital markets research. International Journal of Forecasting, 9, 295–320]. We categorize papers published since 1992, describe the research questions addressed, and suggest avenues for further research in seven broad areas: (1) analysts' decision processes; (2) the nature of analyst expertise and the distributions of earnings forecasts; (3) the information content of analyst research; (4) analyst and market efficiency; (5) analysts' incentives and behavioral biases; (6) the effects of the institutional and regulatory environment (including cross-country comparisons); and (7) research design issues.

A Review of Research Related to Financial Analysts' Forecasts and Stock Recommendations

This paper reviews research regarding the role of financial analysts in capital markets. The paper builds on the perspectives provided by Schipper (1991) and Brown (1993). We categorize papers published mainly since 1992 and selectively discuss aspects of these papers that address or suggest key research topics of ongoing interest in seven broad areas: analysts' decision processes, the determinants of analyst expertise and distributions of individual analysts' forecasts, the informativeness of analysts' research outputs, analyst and market efficiency with respect to information, effects of analysts' economic incentives on their research outputs, effects of the institutional and regulatory environment (including cross-country comparisons), and the limitations of databases and various research paradigms.

Distinguishing between naturally and culturally flaked cobbles: A test case from Alberta, Canada

AbstractDistinguishing between naturally and culturally produced, simply flaked cobbles has been a problem for proponents of a pre‐Clovis occupation in the Americas. Several sites in Alberta have been assigned a pre‐Clovis status based on the presence of simply flaked cobbles found in Late Pleistocene till deposits. Historically, these types of assemblages have been assigned a cultural status based on subjective criteria and appeals to the analyst's expertise. To determine the archaeological status of two such assemblages from Alberta (Varsity Estates and Silver Springs), they were compared to a known natural assemblage and two known cultural assemblages. Chi‐square testing was used to evaluate several lithic attributes. Only those attributes that statistically differentiated between natural and cultural assemblages were used for further analyses. All cobbles were then scored using these attributes. A point was awarded when a statistically significant attribute of human‐manufacture was present. These points were then totaled, providing an aggregate score for each cobble. These scores were plotted to determine whether the test assemblages had closer affinities with the known natural or known cultural assemblages. The results indicate that the proposed pre‐Clovis assemblages have closer affinities to known natural assemblages than to cultural assemblages. Our results suggest that these sites provide no evidence for a pre‐Clovis occupation in the Americas. © 2004 Wiley Periodicals, Inc.

The transformation of failure analysis expertise into a knowledge-based system for NDE specialists : Roberge, P.R. 3rd International Conference on Computer Aided Assessment and Control of Localized Damage, Udine (Italy), 21–23 Jun. 1994. pp. 719–726. Edited by M.H. Aliabadi and A-Carpinteri. Computational Mechanics Publication (1994) ISBN 1853122629

Failure analysis and non destructive evaluation (NDE) are two complementary components of materials engineering. The aim of failure analysis is to determine the primary causes of failures, and recommend action. The analyst must sort through facts gathered during an investigation and classify the information into types of failure. A failure investigation may have various objectives, ranging from affixing blame for a failure to finding the cause of a problem in order to prevent its reoccurrence. Failure expertise is thus specifically centered around the knowledge of failing components and failure characteristics. The complementary focus of a NDE investigation is on the prevention of failures by inspecting components the most susceptible to fail and, in this respect, is an application of the failure analyst expertise. This paper describes the structure of a knowledge shell designed with an object oriented architecture and particularly adapted to the fields of failure analysis, materials performance and NDE.

A Nonlinear Multi-Domain Stress Analysis Method for Surface-Mount Solder Joints

Solder joint fatigue failures are a potential reliability hazard in surface-mount electronic packages under cyclic thermal loading environment. Proper design and reliability assessment are thus crucial to ensure the fatigue endurance of the electronic packages. Accurate modeling of the stress and strain fields within the solder joint under cyclic thermal loading condition is of extreme importance since ultimately, a reasonable fatigue life estimation depends not only on a appropriate fatigue model, but more fundamentally, on accurately predicted stress and strain fields. Modeling stress and strain fields in solder joint in surface-mount electronic packages have never been an easy task since solder undergoes elastic, plastic and time dependent creep during each loading and unloading cycle. Some of the existing closed-form stress analysis models tend to oversimplify this complicated viscoplastic stress state, thus failing to give a reasonable prediction of the solder joint fatigue endurance. Extensive finite element analyses require prohibitive investment in terms of the analysis time and analyst expertise, especially when full scale elastic, plastic and creep analyses are performed. A generalized multi-domain approach proposed earlier by the authors is further developed in this paper to obtain the stress and strain fields in J-leaded surface-mount solder joint undergoing elastic-plastic deformation, under cyclic thermal environment (Ling et al., 1995). The Rayleigh-Ritz energy method based on a multi-field displacement assumption is used. In a previous paper (Ling et al., 1995), the results for analysis within elastic region had been demonstrated and were proved to be in agreement with finite element analysis. In this paper we further develop the methodology into plastic deformation region. Hysteresis loops for both the global and the local CTE mismatch problem can finally be generated. Results for two-dimensional elastic-plastic analysis are presented in the current paper. Creep deformation can be further modeled with this scheme by using time-stepping incremental techniques, and will be presented in a future paper. The final goal of this research is to predict the stress, strain and energy density distributions in the solder joint with reasonable accuracy. The fatigue assessment of the solder joint can then be performed by combining results from this stress analysis model with an appropriate damage model, for example, the energy-partitioning fatigue model (Dasgupta et al., 1992).

Estimates of Macropod Density from Line Transect Surveys Relative to Analyst Expertise

Analysis of line transect data to estimate population density often requires judgments by the analyst. These judgments often must be made without a firm theoretical basis and include issues such as truncation and grouping of the distance data prior to analysis, the choice of model used to estimate the detection function, and methods used to estimate variance components. Our objective was to examine bias and precision of density estimates obtained by experts in distance sampling theory. We asked 7 experts in the field of distance sampling to cooperate in a blind analysis of 6 sets of line transect data on 3 macropod species in Australia where true density was known. Expert analysts chose several different approaches to analyze these 6 datasets. Experts underestimated density by an average of 10-12%, but precision was good; coefficients of variation ranged from 3.4 to 9.3%. Three people, termed students, volunteered to perform a similar blind analysis of these datasets after they completed a 1-week course in distance sampling and analysis theory. While these students had good quantitative skills, we found little difference in their performance compared with that of the experts; both groups underestimated density by 10-12%. The subjective aspects of the analysis of distance sampling may be overcome with some education, reading and experience with computer software. At that point, a person's performance can be near that of experts in the field of distance sampling, at least in analysis of fairly simple datasets, collected under an adequate design, and when assumptions were relatively valid.

Providing descriptive power to guided self-elicitation

Recent exploratory research developed and tested a guided self-elicitation (GSE) methodology. With GSE, an expert is enabled to capture his/her own performed expertise as production rule-instances. GSE is based on published cognitive research, using a production system view of conscious cognitive information processing and certain demonstrated human abilities: for identifying and categorizing perception, rehearsing and reconstituting prior thought processes and verbal reporting of concurrent cognitive information processing. Experimentally self-elicited decision analyst expertise (leading subjective probability assessment interviews) demonstrates that performed expertise can consist of complex rule-processed knowledge forms. An object model for representing complex knowledge forms is proposed and discussed.

Comparing Gel Permeation Chromatography and Ultrafiltration for the Molecular Weight Characterization of Aquatic Organic Matter

Gel permeation chromatography (GPC) and ultrafiltration (UF), both relatively inexpensive analytical techniques requiring moderate levels of analyst expertise, are potential tools for monitoring the presence of aquatic organic matter and humic substances in raw water sources as well as the removal of organic constituents during water treatment. The two methods provided somewhat different trends in the relative molecular weight distribution of dissolved organic matter in various water sources. The GPC method generally indicated a higher molecular weight than the UF method for a given source. Moreover, the GPC method was found to be more significantly affected by pH conditions.