Performance Of Analysts Research Articles

Cross-Industry Information Sharing and Analyst Performance

We identify a specific organizational resource in brokerage houses—information sharing among analyst colleagues who cover economically related industries along a supply chain. After controlling for brokerage selection effects, we show evidence consistent with the benefit of this resource to analyst research performance. Specifically, we find that analysts whose colleagues cover more economically connected industries have better research performance, especially when their colleagues produce higher-quality research. We further show that colleagues’ coverage of downstream (upstream) industries is positively related to the accuracy of only the analyst’s revenue (expense) forecasts and that analysts and their highly connected colleagues tend to issue earnings forecast revisions contemporaneously. Last, we find that analysts with economically connected colleagues tend to have a higher level of industry specialization. Overall, our findings suggest that analysts rely on organizational resources to produce high-quality research. Hence, a portion of their performance and reputation is not transferable across employers.

Macro uncertainty, analyst performance, and managerial ability

This paper documents the systematic and negative effects of heightened macro uncertainty on financial analyst performance and the potential for managerial ability to moderate such negative effects. Specifically, we find that macro uncertainty measures are significantly and negatively correlated with the accuracy and informativeness of analysts’ earnings forecasts and positively correlated with the dispersion of earnings forecasts. Furthermore, we find that the negative effects of macro uncertainty on analyst performance are significantly mitigated in firms with high managerial ability. Our results provide a broad overview regarding the negative effects of macro uncertainty on analyst performance and indicate that managerial ability could be a potential moderator to mitigate the negative impacts of macro uncertainty on analyst performance, which is based on the upper-echelons theory.

How Skilled are Security Analysts?

The majority of security analysts are identified as skilled when the cross-section of analyst performance is modeled as a mixture of multiple skill distributions. Analysts exhibit heterogeneous skill–some are high-type, and some are low-type. On average, the recommendationrevisions of both types exhibit positive abnormal returns. The heterogeneity stems from differential ability to produce new information; all analysts can profitably process news. Top analysts outperform because more of their recommendations are influential (i.e., associated with statistically significant returns) and both their influential and non-influential recommendations are more informative than other analysts'. A majority of research firms are also identified as skilled.

How Skilled Are Security Analysts?

ABSTRACTThe majority of security analysts are identified as skilled when the cross‐section of analyst performance is modeled as a mixture of multiple skill distributions. Analysts exhibit heterogeneous skill—some are high‐type, and some are low‐type. On average, the recommendation revisions of both types exhibit positive abnormal returns. The heterogeneity stems from differential ability to produce new information; all analysts can profitably process news. Top analysts outperform because more of their recommendations are influential (i.e., associated with statistically significant returns) and both their influential and noninfluential recommendations are more informative. A majority of research firms are also identified as skilled.

Peer Effects among Financial Analysts

ABSTRACTWe hypothesize that the arrival of star analysts improves the performance of incumbent financial analysts, while the departure of star analysts has the opposite effect. Our results consistent with this hypothesis are concentrated primarily in the tests related to star arrivals. Our findings are robust to an instrumental variable approach and a falsification test. In addition, we hypothesize that the impact of the arrival/departure of star analysts is more pronounced when the star analyst covers the same industry as the incumbents (especially for industries with high uncertainty), when the star analyst is more established, when the incumbent analysts are less experienced, and when the brokerage house has fewer existing star analysts. Overall, our paper offers evidence of peer effects among financial analysts, mainly through the arrival of star analysts.

Analyzing the Analysts: The Effects of Technical and Social Skills on Analyst Performance and Careers

This paper investigates the effects of financial analysts’ technical and social skills on their performance and career advancement. Using financial analysts’ LinkedIn profiles, we find that analysts with strong technical skills endorsed by LinkedIn connections generate more accurate earnings forecasts and more profitable stock recommendations. Analysts with better social skills, proxied by the number of LinkedIn connections, have better communications with corporate management during conference calls and also produce more accurate earnings forecasts. Their stock recommendations are not more profitable but receive stronger market reactions. Further, these sociable analysts are more likely to be voted as All-Star Analysts and to move to high-status brokerage firms when they change jobs. These findings provide the first large-sample evidence on the different roles of technical and social skills in the financial industry.

Challenges and performance metrics for security operations center analysts: a systematic review

ABSTRACT The increasing use of Security Operations Centers (SOCs) by organisations as a part of their cyber security strategy has led to several studies aiming to understand and improve SOC operations. However, to the best of our knowledge, there is no systematic literature review on the challenges faced by SOC analysts or on metrics for measuring analysts performance. To this end, we conducted a Systematic Literature Review (SLR) in accordance with the guidelines for undertaking SLR and analyzed papers published on SOCs between 2008 and 2018. We provide a comprehensive overview of the challenges faced by SOC analysts and of the metrics suggested in the literature for measuring analysts performance. In addition, we present a mapping between the challenges and existing performance metrics showing how the effectiveness of an analyst in addressing a particular challenge could be measured. We also discuss the drawbacks of the existing metrics and suggest directions for improvement. Our findings will enable SOC analysts and managers, as well as the academic community to gain a better understanding of the challenges impeding the performance of SOC analysts, and how analysts performance could be measured and improved.

Analysts’ Beauty and Performance

We study whether sell-side financial analysts’ physical attractiveness is associated with their job performance. We find that attractive analysts make more accurate earnings forecasts than less attractive analysts. Moreover, more attractive analysts make stock recommendations that are more informative in the short run and more profitable in the long run. Additional analyses reveal that attractive analysts attain their better job performance at least partly through their privileged access to information from firm management. For the sources of the beauty effect, we find that more attractive analysts gain more media exposure, have better connections to institutional investors, and receive more internal support from their employers. Additional evidence suggests that analysts’ physical appearance per se at least partly explains our findings. Overall, our study shows that physical attractiveness has a profound impact on the job performance and information access of sell-side financial analysts. This paper was accepted by Shiva Rajgopal, accounting.

Can Field Crews Telecommute? Varied Data Quality from Citizen Science Tree Inventories Conducted Using Street-Level Imagery

Street tree inventories are a critical component of urban forest management. However, inventories conducted in the field by trained professionals are expensive and time-consuming. Inventories relying on citizen scientists or virtual surveys conducted remotely using street-level photographs may greatly reduce the costs of street tree inventories, but there are fundamental uncertainties regarding the level of data quality that can be expected from these emerging approaches to data collection. We asked 16 volunteers to inventory street trees in suburban Chicago using Google Street ViewTM imagery, and we assessed data quality by comparing their virtual survey data to field data from the same locations. We also compared virtual survey data quality according to self-rated expertise by measuring agreement within expert, intermediate, and novice analyst groups. Analyst agreement was very good for the number of trees on each street segment, and agreement was markedly lower for tree diameter class and tree identification at the genus and species levels, respectively. Interrater agreement varied by expertise, such that experts agreed with one another more often than novices for all four variables assessed. Compared to the field data, we observed substantial variability in analyst performance for diameter class estimation and tree identification, and some intermediate analysts performed as well as experts. Our findings suggest that virtual surveys may be useful for documenting the locations of street trees within a city more efficiently than field crews and with a high level of accuracy. However, tree diameter and species identification data were less reliable across all expertise groups, and especially novice analysts. Based on this analysis, virtual street tree inventories are best suited to collecting very basic information such as tree locations, or updating existing inventories to determine where trees have been planted or removed. We conclude with evidence-based recommendations for effective implementation of this type of approach.

Monitoring and Improving Managed Security Services inside a Security Operation Center

Nowadays, small to medium sized companies, which usually cannot afford hiring dedicated security experts, are interested in benefiting from Managed Security Services (MSS) provided by third party Security Operation Centers (SOC) to tackle network-wide threats. Accordingly, the performance of the SOC is becoming more and more important to the service providers in order to optimize their resources and compete in the global market. Security specialists in a SOC, called analysts, have an important role to analyze suspicious machine-generated alerts to see whether they are real attacks. How to monitor and improve the performance of analysts inside a SOC is a critical issue that most service providers need to address. In this paper, by observing workflows of a real-world SOC, a tool consisting of three different modules is designed for monitoring analysts' activities, analysis performance measurement, and performing simulation scenarios. The tool empowers managers to evaluate the SOC's performance which helps them to conform to Service-Level Agreement (SLA) regarding required response time to security incidents, and see the need for improvement. Moreover, the designed tool is strengthened by a background service module to provide feedback about anomalies or informative issues for security analysts in the SOC. Three case studies have been conducted based on real data collected from the operational SOC, and simulation results have demonstrated the effectiveness of the different modules of the designed tool in improving the SOC performance.

Modeling Analysts’ Recommendations via Bayesian Machine Learning

Individual analysts typically publish recommendations several times per year on the handful of stocks they follow within their specialized fields. How should investors interpret this information? How can they factor in the past performance of individual analysts when assessing whether to invest long or short in a stock? This is a complicated problem to model quantitatively: There are thousands of individual analysts, each of whom follows only a small subset of the thousands of stocks available for investment. Overcoming this inherent sparsity naturally raises the question of how to learn an analyst’s forecasting ability by integrating track-record information from all the stocks the analyst follows; in other words, inferring an analyst’s ability on Stock X from track records on both Stock X and stocks other than X. The authors address this topic using a state-of-the-art computationally rapid Bayesian machine learning technique called independent Bayesian classifier combination (IBCC), which has been deployed in the physical and biological sciences. The authors argue that there are many similarities between the analyst forecasting problem and a very successful application of IBCC in astronomy, a study in which it dominates heuristic alternatives including simple or weighted averages and majority voting. The IBCC technique is ideally suited to this particularly sparse problem, enabling computationally efficient inference, dynamic tracking of analyst performance through time, and real-time online forecasting. The results suggest the IBCC technique holds promise in extracting information that can be deployed in active discretionary and quantitative investment management.

PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method

As the number of seismic sensors grows, it is becoming increasingly difficult for analysts to pick seismic phases manually and comprehensively, yet such efforts are fundamental to earthquake monitoring. Despite years of improvements in automatic phase picking, it is difficult to match the performance of experienced analysts. A more subtle issue is that different seismic analysts may pick phases differently, which can introduce bias into earthquake locations. We present a deep-neural-network-based arrival-time picking method called that picks the arrival times of both P and S waves. Deep neural networks have recently made rapid progress in feature learning, and with sufficient training, have achieved super-human performance in many applications. PhaseNet uses three-component seismic waveforms as input and generates probability distributions of P arrivals, S arrivals, and noise as output. We engineer PhaseNet such that peaks in probability provide accurate arrival times for both P and S waves, and have the potential to increase the number of S-wave observations dramatically over what is currently available. This will enable both improved locations and improved shear wave velocity models. PhaseNet is trained on the prodigious available data set provided by analyst-labeled P and S arrival times from the Northern California Earthquake Data Center. The dataset we use contains more than seven million waveform samples extracted from over thirty years of earthquake recordings. We demonstrate that PhaseNet achieves much higher picking accuracy and recall rate than existing methods.

Do Brokerages Benefit From All-Star Females?

This paper studies whether senior women's representation in the workplace leads to positive outcomes. I find that all-star female analysts positively affect the performance of their peers. Specifically, an increase in the number of all-star females in a brokerage spurs the future performance of other analysts in that brokerage. These results are not explained by unobserved factors that may affect analysts’ sorting behavior and performance. Analysts who cover firms in the same industry as all-star females experience a larger boost in their outcomes, suggesting that knowledge spillover is an economic channel through which all-star females affect their cohorts' performance. Moreover, the timeliness of analysts' forecasts improves following a higher representation of all-star females in their brokerages.

Improving the identification of functional system requirements when novice analysts create use case diagrams: the benefits of applying conceptual mental models

Planning an information system (IS) is challenging, especially for inexperienced IS analysts, including students. One area that often causes particular difficulty is defining the requirements of the future system—a phase that is crucial to the success of any IS project. While serving as advisers for students carrying out their first IS projects, we observed that “imagining” the future system’s functionality and workflow from the user’s perspective makes it easier for students to identify the system’s functional requirements. This insight led us to suggest refining the requirements phase by structuring the “imagining” process into a formal Conceptual mental model (CMM)—that is, a tangible visual representation of the user’s beliefs and expectations (i.e., the user’s mental model) about the system to be developed. We expect that this approach, taken from the domains of cognitive psychology and human–computer interaction, will improve the ability of inexperienced IS analysts to identify the functional requirements of the future system, along with their associated relations and actors. In particular, we expect that this approach will yield better Use case diagrams (UCDs)—a key component of the Use case model for representing and then specifying system requirements, relations and actors. Here, we report on a controlled experiment testing whether creation of a CMM prior to the creation of a UCD improves the identification of functional system requirements, relations, and actors in terms of correctness, completeness, and irredundancy of the UCD. We found that participants who produced a UCD after producing a CMM performed significantly better in defining system requirements, relations and actors, as expressed in their UCDs, compared to participants who produced a UCD without first producing a CMM. We conclude that our suggested refinement improves the performance of novice IS analysts in capturing, analyzing and defining functional system requirements, relations and actors in a UCD, in terms of correctness, completeness, and irredundancy.

MENELUSURI PENGARUH KOMPETENSI PROFESIONAL DAN MOTIVASI KERJA TERHADAP KINERJA ANALIS KREDIT MIKRO PADA LEMBAGA KREDIT MIKRO PERBANKAN MAUPUN LEMBAGA KEUANGAN MIKRO

Abstract:Credit quality and Non performing loan emerged during credit periode depend on credit analyst workindicator. Other factors are any activities done after drawdown bank loan to small and micro entriprizesas well. This research will explore non performing loan based on credit analyst performance. Analystperformance will be depend variable and professional competence and motivation will be independentvariables. Based on regression and correlation analysis, the result of the analysis show that Sig on theAnova tabels show Sig = 0.033 or less than probability value 0,05. The consecuenties Ho is refused anHa accepted. This mean that professional competence and motivation significantly and simultaneouslyinfluences performance. The regression line equation is -122,685 0,122x1 + 0,749x2. Determinantcoefisien or R = 0.5322. That is mean 53,2% analyst performance contributed by professionalperformance and motivation. Proporsion 46,8% will be determined by other unidentified factors.Keywords: Professional competence, Motivation, Credit analyst performance, Regression, Correlation,Credit analyst, Credit committee, Marketing Staf, Salses, Micro Credit.

Human performance modelling for image analyst decision support design

The critical element in human in the loop military systems, image analysts must make decisions quickly. Improving analyst performance remains a priority of military leadership. This model, and previous findings of synthetic aperture radar (SAR) human in the loop experiments were used to design and test a decision support system (DSS) for analysing electro-optical (EO) and SAR images. Cognitive task analysis was used to develop an operator function model of the analyst classification task. Findings indicate that a DSS increased decision confidence and time. Interestingly, analyst trust is higher when employed in the analysis of EO images, yet aided performance was more accurate on SAR images. As uncovered through human performance modelling, analyst accuracy, confidence and time aided by a trusted DSS is crucial to analyst performance. These results suggest while the developed DSS improved analyst confidence and accuracy, it did not do so while concurrently decreasing task time.

Human performance modelling for image analyst decision support design

The critical element in human in the loop military systems, image analysts must make decisions quickly. Improving analyst performance remains a priority of military leadership. This model, and previous findings of synthetic aperture radar (SAR) human in the loop experiments were used to design and test a decision support system (DSS) for analysing electro-optical (EO) and SAR images. Cognitive task analysis was used to develop an operator function model of the analyst classification task. Findings indicate that a DSS increased decision confidence and time. Interestingly, analyst trust is higher when employed in the analysis of EO images, yet aided performance was more accurate on SAR images. As uncovered through human performance modelling, analyst accuracy, confidence and time aided by a trusted DSS is crucial to analyst performance. These results suggest while the developed DSS improved analyst confidence and accuracy, it did not do so while concurrently decreasing task time.

Joint Distribution of Forecasts and Outcomes: Impact of Non-Normality on the Measurement of Forecasting Skill, with Applications to Analysts’ Target Prices

The purpose of this paper is to provide a detailed analysis of the joint distribution of forecasts and outcomes in the context of financial forecasting. We use Edgeworth expansions to model this joint distribution. In turn this allows us to assess the impact of non-normality, either in forecasts, or outcomes or both. This leads to multiple results; we can deduce the distribution of the forecast error; we can analyse the properties of the hit rate (H), as a statistical concept in its own right, and also in its relation to the information coefficient (IC); two tools that are used in the assessment of forecasting ability by active fund managers and financial analysts. Our paper contributes to the recent econometric literature on directional forecasting as well as the empirical literature that examines analyst performance. We find that the close link between H and IC under normality breaks down in the more general case. We provide further evidence on the richness of this approach by looking at simulation and empirical evidence.

Understanding and improving intelligence analysis by learning from other disciplines

Intelligence organizations acquire, evaluate, assess, and disseminate information to support national security and foreign policy decision-making. It is part of a government’s efforts to get as close to complete information as possible about both the operating environment as well as other actors. The methodologies employed by intelligence analysts are similar to yet different from those used in many other academic disciplines and professional fields. This discussion about methodology – a form of comparative applied epistemology – can be used to better understand intelligence analysis as a function of government and improve the performance of intelligence analysts.

Do analysts who understand accounting conservatism exhibit better forecasting performance?

This study investigates the performance of analysts when they match the asymmetric timeliness of their earnings forecast revisions (i.e., asymmetric forecast timeliness) with the asymmetric timeliness of firms’ reported earnings (i.e., asymmetric earnings timeliness). We find that better timeliness-matching analysts produce more accurate earnings forecasts and elicit stronger market reactions to their forecast revisions. Further, better timeliness-matching analysts issue less biased earnings forecasts, more profitable stock recommendations and have more favorable career outcomes. Overall, our results indicate that analysts’ ability to incorporate conditional conservatism into their earnings forecasts is an important reflection of analyst expertise and professional success.